Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Original PaperSiegel1, MPH, PhD Wiseman2, MPH, PhD Alex Budenz1, MA, DrPH Yvonne Prutzman1, MPH, PhD 1Instiute, of 2University of of :of of MedicinePO Box 800717Charlottesville, VA, 22908United StatesPhone: 1 4342438126Email: kara. wiseman@virginia. eduAbstractBackground: Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness. Objective: We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors). Methods: Data came from an experiment (ClinicalTrials. gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the Institute’s quitSTART app. Participants’ (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants’ probability of cessation from 28 variables reflecting participants’ use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals’ SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation. Results: The SML model’s sensitivity (0. 67) and specificity (0. 67) in the held-aside test set indicated that individuals’ patterns of using different app features predicted cessation with reasonable accuracy.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: This more limited role for trees may explain why their total biomass is lower in these systems. Indeed, in CF systems, the plot is periodically cleared and the remaining trees are pruned to allow the planted annual crops to receive full sun, while in SP systems, the growth of grazing grasses is favored by reducing the number of trees. It is also interesting to note that, in line with previous results, the age of the trees in the AFS largely explains the carbon stored in their biomass (Ma et al. 2020). The AFS described in this study reflect different stages of transition in land use, which follow the growth of their trees (Figure S2). For example, a farmer might plant trees in a CF field, which will then become a YA that can still produce annual crops for the first 2 to 4 years, before the shade from the trees becomes too great. The trees are then used as property markers and, as they mature, produce wood and fruits; the YA then becomes an HG or FG. Similarly, our results show that AGC stocks increase from CF to YA, HG and FG (Table 1). The location of AFS in the landscape is not random, and could also explain the observed differences in AGC stocks. For example, most HG are located close to houses, in alluvial valleys that are well supplied with water and where soils are enriched with organic waste and animal manure (Palm et al. 2001). The same applies to FG, which are former HG that have been more or less abandoned, and YA, where the farmer has chosen the site with the intention of turning it into a HG. We therefore expect HG, FG and YA to be located in areas that are more favorable for tree growth (Wagner et al. 2012; Madejón et al. 2016; Treuer et al. 2018). Conversely, CF and SP are mainly located on limestone plateaus where water tables are deeper and soils are shallower, which could reduce tree growth and biomass (Imada et al. 2008). Historical and environmental factors explain differences in SOC stocks We found no significant difference in SOC between the AFS (H1b rejected). This result is inconsistent with our expectation that agroforestry practices affect SOC stocks (Minasny et al. 2017; Wiesmeier et al. 2019), and with previous studies that show significant differences in SOC stocks between AFS (e. g.

Output: pass


Input: Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: An alternative locus for the relatedness effect on cued recall, which we cannot currently rule out, is that competition between the newly acquired meanings and the existing dominant meaning is increased at the time of recall for words with unrelated meanings. While the effects of relatedness on the recognition memory test used during training (Exp. 3, Worksheet 1) and the lexical decision task (Exp. 3) indicate that relatedness effects are not exclusively limited to tasks with an explicit recall component, further experiments will be needed in order to determine the extent to which meaning relatedness might separately influence the learning and recall of novel meanings. In addition to the explicit test of participants’ knowledge of the word meanings, a lexical decision task assessed whether the newly acquired meanings could affect online comprehension. 2 and 3, participants were faster and less error-prone for the words that they had encountered during training, which likely reflects form-based repetition priming (e. g. , Perea and Lupker, 2004). More interestingly, Experiment 3 showed a significant benefit for words whose novel meaning was semantically related to their preexisting meanings (as compared with unrelated meanings). This finding has important implications for the debate within the ambiguity-processing literature about whether and how meaning relatedness affects lexical decision performance. While several authors have reported that responses to ambiguous words with unrelated meanings are slowed relative to related meanings (Azuma and Van Orden, 1997; Beretta et al. 2005; Klepousniotou et al. 2008; Rodd, 2004; Rodd et al. 2002), other authors have reported no effect of semantic relatedness (e. g. , Hino et al. 2010). These inconsistent findings might suggest that previous effects of meaning relatedness could have arisen from some other, potentially confounding difference between the particular sets of words used in the earlier experiments. Laboratory word-learning paradigms provide a useful method for ruling out alternative explanations of between-item differences on response times (e. g. , for studying lexical competition; cf. Bowers et al. 2005, and Gaskell and Dumay, 2003). In the present work, we showed a significant effect of semantic relatedness on lexical decision responses using a within-item design in which the same sets of words were used (across participants) as both related and unrelated items.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: The likelihood ratio test showed that the logistic regression, which included the SML model–predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=. 16). Conclusions: Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps. Trial Registration: ClinicalTrials. gov NCT04623736; apps; machine learning; artificial intelligence; smoking cessation; mHealth; mobile health; app; apps; applications; application feature; features; smoking; smoke; smoker; smokers; cessation; quit; quitting; algorithm; algorithms; mobile phone IntroductionCigarette smoking remains a leading cause of preventable death in the [1]. Evidence-based smoking cessation interventions, though proven to be valuable in helping people quit, are underused [2]. Smartphone apps have the potential to expand the reach and increase the use of evidence-based smoking cessation interventions [1,3]. Smartphone ownership is high among every demographic group in the [4], and an array of smoking cessation apps, including many free options [5], are available in smartphone app stores. Evidence suggests that smoking cessation apps are widely used, with millions of downloads per year [6,7]. Research supporting the use of apps for smoking cessation is still emerging, and many publicly available apps have not been rigorously tested [8]. However, results from randomized controlled trials (RCTs) suggest that apps can be effective in helping people quit smoking [9-11]. Studies have also demonstrated that both higher user engagement in smoking cessation apps [11,12] and longer duration and greater frequency of app use [6] are related to smoking cessation. The many capabilities, features, and functionalities that can be incorporated into smoking cessation apps have the potential to increase their effectiveness. Apps can include interactive and multimedia content, and offer tailored features to meet the needs and p2021. The quitwas conducted to test the effects of incentivizing EMA completion within the quitSTART app on short-term smoking cessation. Participants were English-speaking adults who lived in the United States, smoked cigarettes, and had a self-reported desire to quit smoking.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: , Ramesh et al. 2015). However, it is worth noting that some single-site studies (Gama-Rodrigues et al. 2010; e. g. , Betemariyam et al. 2020) and at least two meta-analyses failed to find any sizable effect of AFS on SOC stocks. Feliciano et al. (2018) highlighted that only 3. 2% of the variance in soil carbon sequestration was explained by the type of AFS, and Shi et al. (2018) found no significant differences in SOC stocks between four different AFS in 354 previous studies. We propose three main explanations to reconcile these different findings. First, in a global meta-analysis, Ma et al. (2020) estimated that it takes only five years for SOC stocks in tropical AFS to reach an equilibrium value. In our study, at least three of the five AFS (namely YA, HG and FG) represent different stages of an AFS transition depending on the age of the trees (Figure S2). Most of these AFS are more than five years old and may have already reached their equilibrium SOC stocks, which could explain why there was little difference between them. Second, we had a limited number of replicates of each AFS within each site, which certainly reduced the power of the statistical tests. We were only able to identify trends, such as higher SOC in SP in Gariuai, which were similar to previous studies (Feliciano et al. 2018). Increasing the number of replicates for each AFS could have increased the power of the tests, and potentially resulted in more significant differences between AFS. Third, other pedoclimatic and historical factors can influence SOC stocks, sometimes to a greater extent than the type of AFS, e. g. , within-site variability in climate, soil texture (Figures S3, S4) or previous land use may have significantly influenced SOC stocks (Wiesmeier et al. 2019; Ma et al. 2020). Our experimental design aimed to limit as much as possible the influence of external co-factors at each site (e. g. , variability in farmers’ management practices, micro-topography, etc. ), but much of the variability cannot be controlled in farmers’ plots.

Output: pass


Input: Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: This finding therefore provides converging evidence that meaning relatedness plays a critical role in determining how ambiguous words are represented and processed. What mechanism(s) underlie the semantic-relatedness effect in the lexical decision component of Experiment 3? First, we must rule out the possibility that this relatedness effect reflects a difference in how well these two types of words are learned. A subset analysis of Experiment 3 that only included those items for which an individual participant had learned something about the new meaning (as measured by the cued-recall task) showed a significant effect of relatedness on the lexical decision task. Importantly, for these “correctly learned” items, the results of the cued-recall task showed that participants could recall equivalent numbers of semantic properties for these two sets of items. This indicates that the semantic-relatedness effect on lexical decision performance is present even for items that are equally well learned. Instead, we propose that the relatedness effect reflects the same underlying mechanisms as the meaning relatedness effects seen for well-established ambiguous words (Beretta et al. 2005; Klepousniotou et al. 2008; Rodd, 2004; Rodd et al. 2002). These effects have been modelled within a distributed connectionist model (Rodd et al. 2004) in which recognition of words with multiple unrelated meanings is delayed by the competition between the words’ mutually incompatible semantic representations, whereas recognition of words with multiple related senses is facilitated by their rich semantic representations. The present experiments cannot conclusively determine the extent to which the relatedness effect represents either facilitation from the related meaning or interference from the unrelated meaning. Future studies that include a set of baseline, unambiguous words that are presented along with their existing meanings during training may address this issue, provided that the studies ensure equal exposure to these existing meanings and to the meanings to be learned. For now, we suggest that the relatedness effect observed here reflects some combination of these two effects. Regardless of the precise mechanisms of the relatedness effect, this result indicates that after 5 days of training using a range of semantically demanding tasks, the new meanings have become sufficiently well integrated into participants’ lexicons that they can influence online recognition of these words. Interestingly, a comparable relatedness effect was not seen in the lexical decision component of Experiment 2, which used a similar length of training (7 days) but relatively superficial rating tasks during training.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Aim 1 AnalysisTo identify which patterns of use of app feature use predict short-term smoking cessation, we built SML models predicting 7-day smoking abstinence from a set of predictor variables that included our binary app feature use variables, our proportion app feature use variables, participants’ total number of app use sessions, phone type (iPhone or Android), and study arm. Phone type was included as a variable in the SML models because the iPhone and Android versions of the quitSTART app were built separately and user data from each app were recorded in a slightly different manner. Although the 2 apps appeared identical to users and we harmonized the user data collected from each, we chose to include phone type as a variable in the SML models in case there was a relationship between phone type and app use or between phone type and cessation. We first randomly divided our data into a training set (n=100, 75% of the data) and a held-aside test set (n=33, 25% of the data). Working with the training set, we used recursive feature elimination with 10-fold cross-validation to determine the optimal number of features for our classifier and then fit our logistic regression classifier using this number of features to the training set. We selected a logistic regression classifier because our outcome variable was binary, and we wanted a classifier that would yield predicted probabilities (rather than binary predictions) for every participant. We evaluated the SML model’s performance in the training set by looking at its sensitivity, specificity, and accuracy. We also examined variable importance (defined as the scaled absolute value of the coefficient of each variable in a logistic regression model for binary classification) for each feature and identified the features in the model assigned the highest importance for predicting cessation. We produced partial dependence plots for each of the top 10 most important features in order to better understand each feature’s relationship with short-term smoking cessation [29]. We then applied the model to the held-aside test set and looked at its sensitivity, specificity, and accuracy. We then used it to produce predicted probabilities of short-term cessation for each participant included in the test set. Aim 2 AnalysisAs a first step toward testing whether participants’ patterns of app feature use predicted unique variance in cessation, we fit 2 logistic regression models using the test set data.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: We found a significant difference in SOC between the two sites, with higher SOC stocks at Gariuai than at Osso-Luga. A priori, three factors that differentiate the sites could explain these differences in SOC stocks: climate (through elevation; Fig. 1; Figure S3), soil texture (Table S1) and history. First, we found that SOC stocks were positively correlated with elevation, both between and within sites (Figure S3). This result is consistent with those of Raich et al. (2006), who found higher SOC at higher elevations in tropical evergreen forests in various countries (e. g. , Congo, Thailand). As changes in elevation are associated with variations in climate (precipitation, temperature), our result is also consistent with previous studies that found higher SOC in areas with higher precipitation (Schuur et al. 2001) and lower temperature (Raich et al. 2006; Marín-Spiotta and Sharma 2013). Second, soil texture is a key driving process for SOC storage in the tropics (Don et al. 2011; Wiesmeier et al. 2019; Matus 2021). In particular, many studies have found a strong positive correlation between SOC stocks and the fine mineral fractions of soils [i. e. , clay or clay + fine silt content; Zinn et al. (2005)]. In our study, we found no significant correlation between SOC stocks and clay + fine silt content when site and depth were included in the model (Figure S4). Furthermore, the Gariuai soils, which had the highest SOC stocks, were also the least clayey. This contradicts the typical (positive) relationship between the fine mineral fraction and SOC stocks. It is therefore unlikely that the difference in SOC stocks between the two sites can be attributed to soil texture. Thirdly, history could be a significant driving factor. Gariuai was continuously inhabited for over 50 years, whereas Osso-Luga was abandoned for almost three decades. The management and care of the AFS in Gariuai, such as pruning, organic inputs related to the presence of livestock, as well as optimization of the different strata in terms of C input, may have led to an increase in soil organic carbon compared to abandoned systems (Lorenz and Lal 2014).

Output: pass


Input: Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: We therefore speculate that a high level of semantic engagement may be a necessary requirement for participants to integrate newly acquired meanings with their existing lexical knowledge. It may seem surprising that these newly learned meanings can influence participants’ lexical decision responses to well-established, highly familiar words with a single, strongly dominant meaning (e. g. , “ant” or “path”). Participants have received relatively little exposure to the novel experimental meanings, as compared with their lifetimes’ experience with the preexisting dominant meanings, and reading time experiments have consistently shown that subordinate meanings have minimal impact on processing unless they are strongly supported by the sentence context (e. g. , Duffy, Morris, & Rayner, 1988). The effect is particularly striking given that participants had not encountered the new meanings since the previous day. This finding is consistent with data from recent priming experiments (Rodd et al. 2012) that have shown that participants’ relative p504. doi:10. 1006/jmla. 1997. 2502 459. doi:10. 3758/BF03193014 65. doi:10. 1016/j. cogbrainres. 2004. 12. 006 85. doi:10. 1037/0278-7393. 22. 1. 63 54. doi:10. 1016/j. cognition. 2005. 02. 002 4883. 608. doi:10. 1016/S0022-5371(83)90364-X 3800. doi:10. 1098/rstb. 2009. 0111 446. doi:10. 1016/0749-596X(88)90066-6 39. doi:10. 1111/j. 1467-9280. 2007. 01845. x 613. doi:10. 1080/01690960601017013 132. doi:10. 1016/S0010-0277(03)00070-2 196. doi:10. 1037/a0020475 PubMed 1062. doi:10. 1037/0278-7393. 20. 5. 1051 1247. doi:10. 1037/0096-1523. 20. 6. 1233 1543. doi:10. 1037/a0013012 353. doi:10. 1016/j. cogpsych. 2007. 01.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: As can be seen in Figure 1, this feature was negatively related to the probability of cessation, indicating that users who reported “slipping up” more often, proportional to their app use, were less likely to successfully quit smoking. The second and third most important features in the model, respectively, were nexplorecontentpages_prop and nbadgescompleted_prop. The former variable represents the number of times a participant viewed “Tips,” “FYIs,” or “Inspirations” content pages in the app divided by their number of app use sessions. The latter represents the number of badges a participant earned for reaching milestones in their cessation journey or use of the quitSTART app (eg, checking the app 5 times in 1 day) divided by their number of app use sessions. Both of these variables were positively related to cessation, showing that participants who used these app features more often were more likely to successfully quit smoking. Other features that were among the top 10 with the highest variable importance were naddlocation_prop, ncompletedemas_prop, studyarm, nprogresspressed_prop, noquitdate_bin, and nfeelinggreatpressed_prop. After building our SML model and assessing feature importance in the training set, we fit the model in the test set. The model’s accuracy was 0. 67, and both its sensitivity and specificity were also 0. 67. We retained the SML model–predicted probabilities of cessation as a variable in the test set. Results from the 2 logistic regression models performed in the test set are summarized in Table S1 in Multimedia 1. The likelihood ratio test comparing model 1, which included our set of participant characteristics that are known to be related to cessation, to a null model was not statistically significant at the α=. 05 level (χ29=5. 0; P=. 84). Likewise, the likelihood ratio test comparing model 2, which included all variables included in model 1, as well as the predicted probabilities of cessation from the SML model to a null model was not statistically significant (χ210=7. 0; P=. 73). The likelihood ratio test comparing model 2 to model 1 was not statistically significant (χ21=2. 0; P=. 16), indicating that model 2 provided a statistically equivalent fit to the data to model 1.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: SOC stocks are not explained by variation in AGC stocks We found no significant relationship between AGC and SOC stocks in either the topsoil layer 0–10 cm depth (H2a refuted) or at 0–30 cm depth (H2b confirmed). Overall, AGC stocks were a poor predictor of SOC stocks. These results may relate to previous findings that SOC stocks saturate much faster than AGC stocks in tropical AFS. For example, in their global meta-analysis, Ma et al. (2020) estimated that it took only five years for SOC stocks in tropical AFS to reach an equilibrium value, but more than three decades for AGC stocks. This desynchronization of SOC and AGC stocks recovery has also been observed in naturally regenerating tropical forests. In a systematic review, Martin et al. (2013) found that SOC in tropical secondary forests changed little with time after disturbance, while AGC increased. Ojoatre et al. (2024) found similar results in a secondary tropical mountain forest. These results suggest that there is little correlation between AGC and SOC stocks once AFS are more than five years old, which is the case in our study for FG, HG (> 5 years old) and partially for YA (2 to 10 years; Table S2). This saturation of SOC stocks even with increasing AGC stocks is counterintuitive, as it might be expected that higher AGC stocks would lead to more litter production and decomposition, some of which would increase SOC stocks. However, Sayer et al. (2019), found that SOC stocks did not increase in a lowland tropical forest in Panama after 15 years of an experimentally doubled aboveground litter input. Two processes could explain these findings. On the one hand, the addition of easily decomposable litter could stimulate microbial decomposition and turnover of old stored SOC [i. e. , priming effect; Sayer et al. (2019)]. On the other hand, the capacity of the soil to store SOC through organo-mineral associations would be limited by its fine element content (clay + fine silt) (i. e. , saturation concept). In addition to biomass stocks, other vegetation characteristics may account for variations in soil carbon stocks between AFS. For instance, previous studies have shown that tree diversity can enhance SOC sequestration in natural ecosystems (Chen et al. 2018; Spohn et al.

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: between reading comprehension and phonetic and non phonetic reading skills in children with normal hearing and children with cochlear implants Submit manuscript. . . Journal of eISSN: 2379-6359 Otolaryngology-ENT 5. DOI: 10. 15406/joentr. 2023. 15. 00517 primary purpose of this study was to explore the reading comprehension skills and phonetic and non-phonetic reading skills in the English language in both, children with normal hearing and children with cochlear implantation. Research in this area is limited with respect to the Indian context and hence a study dealing with these aspects has the potential to broaden our understanding of benefits of cochlear implantation as it helps with academics. The study involved a total of 74 participants across three grades, 5th, 6th and 7th of which 57 were CWNH and 17 were CWCI. All were assessed based on scores attained on 4 assessment tasks, phonetic reading skills, non-phonetic reading skills, short reading passage comprehension and long reading passage comprehension. On statistical analysis, there was a significant difference between scores obtained on phonetic reading, short reading passage comprehension and long reading passage comprehension between 5th and 6th as well as 6th and 7th graders of CWNH. However, no significant difference was recorded between all three standards (5th, 6th and 7th) on all 4 assessment tasks for CWCI group. When grade level comparisons were made between CWNH and CWCI, there was no statistically significant difference of scores on any of the 4 tasks between the two groups at any grade level except that 6th graders of CWNH group performed better in the short reading passage comprehension task as compared to 6th graders of CWCI. Keywords: cochlear implant, reading comprehension, phonetic words, non-phonetic words Abbreviations CWNH, children with normal hearing; CWCI, children with cochlear implantation single best predictor of reading success has been known to be phonological awareness. That is, children need to be aware of the sounds of speech in order to acquire knowledge of sound-letter correspondence and use this knowledge to decode the printed word. If a child is not aware of the sounds contained in a word, the child will have difficulty associating sounds with letters.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: The variables considered for inclusion in our sensitivity analysis SML model are summarized in Table S2 in Multimedia 1. Recursive feature elimination showed that the optimal number of features to include in the model was 28. The model’s accuracy in the training set was 0. 88, its sensitivity was 0. 94, and its specificity was 0. 71. The most important feature of the model was studyarm, which represented the participants’ assigned study arm. The importance metrics for each feature included in the model are displayed in Figure S3 in Multimedia 1. The model’s accuracy in the test set was 0. 64. Its sensitivity was 0. 75 while its specificity was 0. 33. We fit 2 logistic regression models in the test set (see Table S3 in Multimedia 0. 6; P=. 46), indicating that model 2, which included the predicted probabilities from the SML model using continuous app feature use variables, did not provide a significantly better fit to the data than model 1. Figure 1. Partial dependence plots depicting the predicted marginal effects on the probability of cessation for the 10 app use variables assigned the highest feature importance. The x-axis in each figure is constrained to show only values of each variable that were observed in the training set used to build the supervised machine learning model. DiscussionPrincipal FindingsWe developed and tested a novel approach to using SML to examine whether and how the use of specific features within a smoking cessation app predicts short-term cessation. We applied SML models to data from the quitto identify patterns of app feature use that predict short-term smoking cessation. Our analysis of variable importance within this model indicated that the 3 app feature use variables that were most important for predicting cessation were the number of times participants pressed the “I Slipped” button, the number of times they viewed the “Tips,” “FYIs,” or “Inspirations” content pages, and the number of badges they completed (each expressed as a proportion of total app use sessions). We then used a likelihood ratio test comparing 2 logistic regression models to assess whether including patterns of app feature use in our models allowed us to better predict cessation.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: 2023) and in AFS (Islam et al. 2015; Manaye et al. 2021). Moreover, total SOC may be less sensitive to management practices (such as biomass inputs) than specific fractions of SOC (Haynes 2005). Therefore, it would be interesting to continue with further studies that focus not only on the quantity but also the quality of soil organic matter and its relationship with biodiversity (flora and fauna) in traditional Timorese AFS. How to conserve and improve carbon stocks in traditional AFS in Timor-Leste? Although agroforestry can make agriculture more resilient to climate change while improving its carbon footprint and biodiversity conservation value (Cardinael et al. 2021), poor adaptation to the local context can explain many failures to adopt new agroforestry techniques (Coe et al. 2014). Conversely, some traditional agroforestry techniques have evolved in a particular context (biophysical, social and economic) and have adapted to this context (Gouyon et al. 1993; Aumeeruddy 1994; Peltier 1996). This is the case of the traditional AFS studied here in Timor-Leste. Their heritage and socio-economic values (as shown by Cogné and Lescuyer 2024), in addition to their high carbon stock value, confirm the importance of their conservation, both nationally and internationally. These traditional AFS should be conserved to prevent them from being abandoned as the trees wither or their productivity declines. Clear-cutting old agroforests for full-sun crops can appear to be an attractive short-term strategy, providing a large cash inflow from the sale of wood products, followed by four or five years of agricultural production on fertile land. However, this practice unfortunately results in a significant loss of biodiversity and carbon stored in the biomass, and can lead to an irreversible loss of fertility on the plot through soil degradation and erosion. This phenomenon has been documented in a similar context in Sumatra (Indonesia), where Gouyon et al. (1993) highlighted the fact that old agroforests, which they called “jungle-rubber”, and which are quite similar in structure to “forest gardens” (except for the rubber tree component, which has been replaced by other species in Timor-Leste), were being cleared in favor of more intensive production systems.

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: AbstractStudents acquiring Language (ASL) as a second language (L2) struggle with fingerspelling comprehension more than skilled signers. These L2 learners might be attempting to perceive and comprehend fingerspelling in a way that is different from native signers, which could negatively impact their ability to comprehend fingerspelling. This could be related to improper weighting of cues that skilled signers use to identify fingerspelled utterances. Improper cue-weighting in spoken language learners has been ameliorated through explicit phonetic instruction, but this method of teaching has yet to be applied to learners of a language in a new modality (M2 learners). The present study assesses this prospect. Eighteen university students in their third-semester of ASL were divided into two groups; one received explicit phonetic training, and the other received implicit training on fingerspelling. Data from a fingerspelling comprehension test, with two experimental conditions and a control, were submitted to a mixed effects logistic regression. This revealed a significant improvement from the pre-test to post-test by students who received the explicit training. Results indicate that even short exposure to explicit phonetic instruction significantly improves participants’ ability to understand fingerspelling, suggesting that ASL curricula should include this type of instruction to improve students’ fingerspelling comprehension abilities. I IntroductionFingerspelling is a loanword system for borrowing orthographic representations of English words into Language (ASL). It is very often used for proper names and brand names that do not have conventional lexical signs (Padden, 1998). It can also be used for emphasis or even to borrow the exact semantics of an English word into an ASL sentence where the closest translation equivalent is still slightly different. As with all linguistic systems, fingerspelling has multiple levels of linguistic structure. The phonology of signed languages generally includes specifications for handshape, movement, location, and orientation of the palm (Battison, 1978; Stokoe, Casterline & Croneburg, 1965; Valli, Lucas, Mulrooney, & Rankin, 2011). In fingerspelling, the phonological level – handshape and orientation specifications – consist of abstract units that are contrastive with one another (-a- versus -s-, -m- versus -n-, or -h- versus -u-).

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: The results of this likelihood ratio test showed that the logistic regression model that included both the SML-predicted probabilities of cessation based on participants’ app feature use, as well as a set of variables reflecting participants’ baseline tobacco use and demographic and personal characteristics did not fit the data better than a model that included only the latter variables. This means the accuracy of our model predicting whether participants quit smoking was not improved by including the SML-predicted probabilities. However, because only observations from the held-aside test set (n=30) were included in this analysis, the small n likely contributed to this null result. This study adds to the small but growing body of literature that has gone beyond looking at the overall relationship between smoking cessation app use and smoking cessation to examine which specific app features are associated with cessation [20,21]. Some of our findings align with those from prior research. For example, our finding that completing badges is an important variable for predicting smoking cessation aligns with the finding reported by Rajani et al [20] that participants’ frequency of use of gamification features, including earning badges, was associated with motivation to quit. However, there is a need for more research investigating different app features within smoking cessation apps to help maximize the potential public health impacts of smoking cessation apps. The methodological approach developed in this study could be used to guide additional research evaluating smoking cessation apps and to improve the design and refinement of such apps. While this study focused on smoking cessation, this approach could also be applied in research on apps focused on other health behaviors. Our methodological approach could help guide further research in several ways. For example, our finding that patterns of app feature use did not predict unique variance in cessation might lead researchers to explore whether there is variability in the extent to which different groups of app users are helped by different app features. Alternatively, finding that patterns of app feature use did predict unique variance in cessation might inspire additional research investigating users’ perceptions of, satisfaction with, and reasons for using the app feature use variables that were found to be important for predicting cessation. Additionally, if an app feature uses a variable that was expected to be effective based on theory and prior research was not found to be important in predicting cessation, researchers might investigate why this was the case, considering possible explanatory factors such as design and usability issues [15,18]. This research could also inform the design of new apps, as well as the refinement of existing apps.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: This trend toward clearing jungle-rubber was subsequently confirmed by Ekadinata and Vincent (2011), and its impact on carbon stock decline was quantified by Villamor et al. (2014). Strategies to avoid the clearing of traditional AFS could involve new ways of intensifying production while maintaining high carbon and biodiversity values, and gradually renewing the tree population in the plots. For example, it would be interesting to improve tree regeneration by clearing about one hundred square meters (corresponding to the felling of 1 to 10 trees, depending on their crown area). In these clearings, young trees of species considered interesting for their future production could be associated with first heliophile crops (e. g. , cereals), and then shade-tolerant crops (e. g. , taro). Additional mechanisms to encourage the renewal of traditional AFS could include the provision of subsidies (e. g. , payment of carbon credits) or low-interest loans to farmers wishing to renew their agroforestry plots. Another strategy, which has been successfully implemented in other tropical countries (e. g. , for agroforestry coffee), is to improve the incomes of AFS farmers through ‘sustainable agriculture’ certification (Bertrand et al. 2019), although this is primarily applicable to the production of export goods, whereas the AFS studied here are currently oriented toward local consumption and domestic markets. ConclusionAgroforestry systems in the Baucau municipality of Timor-Leste had variable but overall high carbon stocks. These systems have only recently been described in the scientific literature, and more research is needed to improve our knowledge of their diversity and functioning. It is therefore important that all stakeholders, including local authorities, universities, and NGOs, are aware of their existence, diversity, and environmental and social value, in order to propose possible innovations to maintain or improve their functioning. DeclarationsAuthor ContributionAll authors contributed to the design of the sampling protocol. MC carried out the fieldwork. CP analyzed the data with the help of MC and VF. CP prepared the manuscript (including figures and tables) and all authors contributed to the drafting and revision of the manuscript. AcknowledgementWe are grateful to (Cirad) for his facilitation of the field studies and critical revision of the paper, and to the trainees who helped with field data collection: Santos (vegetation) and (soil).

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: a The explicit training program: Experimental groupIn between their pre- and post-tests, participants in this training group received an explicit fingerspelling training. There are two main sections. The first includes information about the structure of fingerspelling. For example, consider the images in Figure 2. These images draw participants’ attention to the differences between hold and transition segments. In hold portions of the signal, the video is clear and little-to-no movement, identified by blurring, is visible. Transition segments do exhibit blurring, and the configuration of the hand shows features of the previous and subsequent letters. This means one could potentially use transition information to predict subsequent letters, leading to faster lexical recognition. 2b the letters -s-, -o-, -r-, and -t- can be identified, while in Figure 2c the first image is slightly blurry and looks to have features of both -s- and -o-, the second with features of -o- and -r-, and the final one with -r- and -t-. These images are described in detail in the explicit training. Figure 2. Example token in still images, extracted from videos, of the word s-o-r-t. The clear video (a) presents participants with unmodified stimuli: all portions of the signal are present. The holds only condition (b) presents stimuli in which the frames of transition are masked, and the transitions only condition (c) provides the opposite: the frames in which a posture is held are masked. The second part of the training focused on describing the types of phonetic variation that students might encounter when they see fingerspelling. Consider the two words in Figure 3. Both include a u-r bigram (two-letter sequence). To make this otherwise hard-to-differentiate letter combination more distinguishable, this signer adds ulnar deviation (sideways flexing of the wrist toward the pinky) and supination (upward rotation) of the forearm, which, taken together, indicate the appearance of -u- and -r-. In both the Figure 3a and Figure 3b examples, neither letter is realized in its canonical form, but this extra movement makes it clear that both letters are intended, as it does not occur with either letter individually, nor with the combination -r- and -u-. Figure 3.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-3634865/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractToday, it seems natural to discuss or, if you prefer, Machine Learning. Many observers of digital marketing and tourism trends in recent years have not missed an opportunity to emphasize how these technologies will become increasingly prevalent and pervasive in our digital age, especially in the travel and tourism sector. But what are machine learning and deep learning? They are branches of artificial intelligence that provide computers with the ability to ’learn without being explicitly programmed’. In essence, when properly trained, artificial intelligence can respond to novel situations based on reasoning and past experiences. This technology, particularly artificial intelligence in tourism, paves new pathways allowing the automation of tasks that were previously done manually. Even in the hospitality industry, it can be successfully deployed for guest assistance tasks, both before and during their stay. Having an assistant on the site communicating with the visitor in real-time without any delay is an effective way to convert them into a customer. Statistics are confirming a correlation between response time to inquiries and the site’s conversion rate. Moreover, during the stay, the use of chatbots has proven its worth. Having a continuously active assistant ready to respond is undoubtedly a boon for the establishment. It also includes activities where there’s a need to understand the guest’s requirements and make decisions, such as recommending services to the guest or preparing a quote. Fortunately, the future will not solely consist of chatbots and robots. A new era of tourism is on the horizon, where humans will spend less time on repetitive tasks and more time catering to and entertaining guests in ways that, for now, only we humans can. In this study, we utilized data from several hotel reviews. Each review represents a client’s assessment of a hotel. For each textual review, we aim to predict whether it corresponds to a positive review (the customer is satisfied) or a negative review (the customer is dissatisfied). The overall ratings of the reviews can range from 2. 5/10 to 10/10. To simplify the issue, we’ll categorize them as follows: negative reviews have overall ratings of less than 5; positive reviews have ratings of 5 or higher.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: We would also like to thank the 31 farmers who gave their time to take part in the interviews and field measurements. A first English version of this paper was proofread by Grace Delobel. This project was co-funded by the and the Ministry for and Development (BMZ) as part of the Partnership for (PSAF) - Ai ba Futuru project, implemented by the für (GIZ). Additional funding for this study was provided by the in Jakarta and the of Timor-Leste. Data AvailabilityAll data collected for this study are available in the following private Dataverse repository: R codes used to analyze the data and generate the manuscript (including figures and tables) are provided as supplementary files (manuscript file). Upon acceptance of the 27. (1994) Local representations and management of agroforests on the periphery of (2022) A global overview of studies about land management, land‐use change, and climate change effects on soil organic carbon. Biology 28:1690–1702. Bertrand B, Breitler J-C, Georget F, et al (2019) The agroecological transition of agricultural systems in the Global South. éditions (2020) of in Systems in Ethiopia. Small-scale Forestry 19:319–334. (2020) Above-ground carbon stocks and timber value of old timber plantations, secondary and primary forests in southern Ghana. and Management 472:118236. (2021) Climate change mitigation and adaptation in agriculture: Why agroforestry should be part of the solution. Agriculture, Ecosystems & Environment 319:107555. (2004) Error propagation and scaling for tropical forest biomass estimates. Philosophical transactions of the of B, Biological sciences 359:409–420. Chave J, Réjou-Méchain M, Búrquez A, et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Biology 20:3177–3190. (2018) Plant diversity enhances productivity and soil carbon storage. Proceedings of the of Sciences 115:4027–4032. (2014) Scaling up agroforestry requires research ’in’ rather than ’for’ development. in 6:73–77.

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: (a) c-o-u-r-s-e and (b) s-o-u-r-c-e naturally fingerspelled. Figure (a) exhibits both ulnar deviation and supination of the forearm, while (b) exhibits the former only. This movement epenthesis is consistent with Brentari’s (1998) proposed phonological rules governing local lexicalization, or words that become appreciably reduced phonologically but only in a particular discourse. Long, academic terms for which there is no conventional sign are prime candidates for local lexicalization. One likely explanation for the development of epenthetic movement with -u- and -r- but not the reverse, -r- and -u-, is because of bigram frequency; -u- and -r- is the 50th most frequent English bigram, occurring in 0. 54% of words, while -r- and -u- is far less common, occurring in only 0. 128% of English words (Norvig, 2015). Other examples of gross movements to attend to in fingerspelling include epenthetic wrist flexion (bending downward) with the appearance of -y- word internally or word finally, and a blending of features of the letters -g-, -h-, and -t-, pictured in Figure 4. This training lasted approximately 30–40 minutes. Figure 4. Two images of the transition between -g-, -h-, and -t- in the fingerspelled word n-i-g-h-t. Image (a) shows the orientation shift (supination) required by the letters -g- and -h-. Image (b) shows outward rotation (pronation) of the forearm, or returning to the default, palm-out orientation. Both images show the anticipation of the letter -t-, which requires the index and middle finger to be separated, to allow for insertion of the thumb between them. Also note that in (a), the pinky remains partially extended from the previous letter -i-, while in (b) it has returned to a more flexed position. Knowledge of anticipatory and perseveratory coarticulation would help in comprehension of words with this letter combination. b The implicit training program: Control groupStudents in the control, or implicit training group, also received training in fingerspelling.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: The challenge lies in predicting this information using only the raw textual data of the review. Hospitality and 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 10Figure 11Figure 12Figure 13Figure 14Figure 151 IntroductionIn the modern era, with the dawn of the digital age, we’re seeing the rise of novel unstructured data. Coupled with the conventional structured data, it has paved the way for the revolution. Concurrently, the progress in computational capabilities and swift advancements in algorithmic designs have given birth to sophisticated analytics, surpassing the usual to extract more profound understandings and forecast future trends. Each technique possesses its unique merits and demerits, tailored to address distinct challenges. By harnessing this rich array of techniques, the tourism sector can discern previously hidden patterns and relationships, perform real-time analysis, and dive into the depths of insights to more comprehensively grasp and elucidate various phenomena, systems, and mechanisms, among others. Notably, the forward-looking capability of certain methodologies enables us to pose and answer questions about the future (Weihs - Ickstadt, 2018). Within the tourism sector, manifests its capabilities through avenues such as travel route enhancement, predictive modelling for trend projections, bespoke experience algorithms, sentiment evaluation and feedback extraction, and real-time alert and surveillance systems, among others. The statistical representation of tourism is particularly challenging, as it pertains to measuring a phenomenon that does not concern a single sector but a set of services, some of which are highly heterogeneous. Tourism can be analyzed from both a demand and supply perspective and is thus measured through various information sources. In general, statistics that look at tourism from the supply side refer to the capacity and occupation of accommodation facilities, while those that measure the tourism phenomenon from the demand side are based on information directly collected from travellers at borders or through traditional household surveys. The tourism domain is intricate and multifaceted, making it a prime arena for the deployment of (AI) solutions that leverage voluminous datasets. Given the multitude of individuals, operations, and activities encapsulated within, data in tourism often falls within the big data category. Furthermore, the potential seems boundless regarding the variety of data origins and AI methodologies that can be harnessed to scrutinize and enhance both the provision and consumption of tourism services, spanning from marketing tactics to over-tourism management.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: Cogné M, (2024) The role of agroforestry in farmers’ strategies and its contribution to the well-being of rural people in Timor-Leste. and Livelihoods 00:1–26. (2011) Impact of tropical land-use change on soil organic carbon stocks - a meta-analysis. Biology 17:1658–1670. (2021) Agroforestry contributions to smallholder farmer food security in Indonesia. 95:1109–1124. Ekadinata A, (2011) Rubber agroforests in a changing landscape: analysis of land use/cover trajectories in Bungo district, Indonesia. Forests, Trees and Livelihoods 20:3–14. (2012) Harmonized world soil database (version 1. 2). FAO, Rome, Italy and (2018) Which agroforestry options give the greatest soil and above ground carbon benefits in different world regions? Agriculture, Ecosystems and Environment 254:117–129. (2014) Useful tropical plants Gama- (2010) in in Bahia, Brazil. 45:274–283. (2010) Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proceedings of the of Sciences 107:16732–16737. Gouyon A, de, (1993) Does ‘jungle rubber’ deserve its name? An analysis of rubber agroforestry systems in southeast Sumatra. 22:181–206. Harvey CA, Chacón M, Donatti CI, et al (2014) Climate‐Smart Landscapes: Opportunities and Challenges for and Mitigation in Tropical Agriculture. 7:77–90. Haynes RJ (2005) as of the Quality of Agricultural Soils: An Overview. pp 221–268 (2006) Timorese plant names and their origins. de Linguı́stica (2013) Tree height and tropical forest biomass estimation. Biogeosciences 10:8385–8399. (2008) Water table depth affects Populus alba fine root growth and whole plant biomass. 22:1018–1026. (2015) Effect of on in the System of North-Eastern Bangladesh. Small-scale Forestry 14:91–101. (2004) Soil carbon sequestration impacts on global climate change and food security.

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: The first research question asks whether the explicit training will help students improve more on a fingerspelling comprehension measure than those students who received the implicit training. The model reveals that students with explicit training improved significantly from their pre- to post-test by a statistically significant margin (p < 0. 05). The second question asks whether the training, which focuses on drawing students’ attention to information contained in the transition segments through descriptions of phonetic variation, impacts performance in all experimental conditions equally. No such interaction was found, which will be discussed in Section IV. These results can be seen visually in Figure 5. Two important features of this figure to note are: (1) Student performance on the pre-test is roughly equal across groups, and (2) students in the explicit training group improve across conditions on the post-test. This is supported by the statistical analysis detailed above. Figure 5. Model predictions plot. Dots represent model predictions and lines represent 95% confidence intervals. The x-axis shows the two types of test. Within each condition, the pre-test is always on the left and the post-test is always on the right. Conditions are labeled at the top: allClearA has video of the full word with no masking (control condition); holdsOnly and transitionsOnly have masking such that the holds or transitions are the only visible portions of the signal (experimental conditions); and allClearB has the full video – like allClearA – and serves as a second control condition. The y-axis represents the probability of a correct response in each condition, on both tests, and for each group of participants. Values for the implicit group in red, appear on the left, and values for the explicit group in blue appear on the right. IV DiscussionThe goal of this study was to determine whether explicit phonetic instruction would benefit adult students acquiring ASL as a second language, with a particular focus on fingerspelling comprehension. Specifically, this study assesses whether knowledge about the structure of fingerspelling – and the ways in which ASL phonology (abstract units including handshape, location, movement, and palm orientation) can vary when it is realized phonetically (how these units are physically produced) – will have a positive impact on comprehension performance. Previous work has explored fingerspelling comprehension but this is the first to apply it to teaching practices.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Such innovations can also aid in rebounding from challenging periods and offer numerous other benefits. Building on vast experiences and initiatives in this sphere, we can outline several insights and provide guiding principles: Regarding the numerous data collection technologies, those that identify thelocation of people, or similarly, any type of phenomenon, are highly relevant for the geography of tourism. New technologies and tracking techniques, including GPS, are emerging. Intelligence and expansive data analytics can addressthe prevalent issue of fragmentation in tourism-oriented mobile applications. A common challenge arises when such apps overly specialize, lacking in holistic personalization. This limitation often stems from their reliance on isolated datasets, rather than catering to the comprehensive requirements of the stakeholders. Considering the pivotal role communication plays within the tourism sec-tor, beyond mere content analysis, there’s potential to deploy generative models rooted in to bolster content generation endeavours. Thus, the advent of has significantly changed the perspective towards the applications of computational power in everyday life. Thanks to the strong collaboration of Statistics, Computer Science, and Engineering, machines can now adapt to the evolving environment and easily optimize problems that were previously non-computational. Frequent developments in the field of data mining and have further boosted the applications of computing power in more diverse fields. Therefore, this procedure is defined as a ”data mining” technique that transforms original data into a format readable and understandable by the model. Data preprocessing, is the first step that marks the beginning of the construction process of a model. Typically, real-world data is imprecise, incomplete, inconsistent (contains outliers or errors), and often has null values referring to specific attributes. Data preprocessing is pivotal as it refines, structures, and arranges unprocessed data, priming them for integration into (ML) algorithms. Serving as the foundational framework for Artificial Intelligence, ML frequently forms the bedrock for data science initiatives, intertwining aspects of computer science and statistical methodologies. The objective is to cultivate algorithms and theoretical constructs rooted in statistical paradigms, targeting elevated predictive prowess (Jordan - Mitchell, 2015). Consequently, ML algorithms construct predictive frameworks adept at correlating user attributes to specific classifications (or determinations) following a learning process.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: Science 304:1623–1627. (2005) Forest soils and carbon sequestration. and Management 220:242–258. (2014) Agroforestry systems: helping smallholders adapt to climate risks while mitigating climate change. 5:825–833. Lawrence D, (2015) Effects of tropical deforestation on climate and agriculture. Change 5:27–36. (2018) Tree size and climatic water deficit control root to shoot ratio in individual trees globally. 217:8–11. Lorenz K, (2014) Soil organic carbon sequestration in agroforestry systems. A review. Agronomy for 34:443–454. Lundgren B, (1982) Sustained agroforestry. In: Agricultural research for development: Potentials and challenges in asia. pp 24–29 (2020) Carbon accumulation in agroforestry systems is affected by tree species diversity, age and regional climate: A global meta-analysis. and Biogeography 29:1817–1828. Madejón P, Alaejos J, García-Álbala J, et al (2016) Three-year study of fast-growing trees in degraded soils amended with composts: Effects on soil fertility and productivity. Journal of 169:18–26. (2021) Tree diversity and carbon stocks in agroforestry systems in northern Ethiopia. and Management 16:14. (2023) World reference base for soil resources (WRB). In: Encyclopedia of soils in the environment. Elsevier, pp 206–217 Marín‐Spiotta E, (2013) Carbon storage in successional and plantation forest soils: a tropical analysis. and Biogeography 22:105–117. (2020) Land‐use history determines ecosystem services and conservation value in tropical agroforestry. 13: (2013) Carbon pools recover more quickly than plant biodiversity in tropical secondary forests. Proceedings of the B: 280:20132236–20132236. Matus FJ (2021) Fine silt and clay content is the main factor defining maximal C and N accumulations in soils: a meta-analysis. 11:6438. (2007) Database - (2017) Soil carbon 4 per mille. Geoderma 292:59–86.

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: Geer and Keane (2014) and Keane and Geer (2016) suggest that perhaps one reason adults acquiring ASL as a second language struggle with understanding fingerspelling is related to incorrect cue-weighting. This problem has been noted for learners of spoken languages as well, and explicit instruction has been found to be successful in helping students to improve. That technique is therefore assessed here with ASL learners. This study examined two research questions, which will be summarized and discussed in Section IV. 1. Section IV. 2 addresses ways in which the present study can be extended in the future through additional studies. 1 Summary of resultsWhile explicit training has been shown to be effective for users of spoken languages acquiring a second spoken language, the same has yet to be shown for learners of a second language in a new modality. The first research question asks whether the explicit training will result in a more significant improvement in fingerspelling comprehension scores than students who were trained implicitly. This was in fact the case. Students in the explicit training group improved significantly from the pre- to post-test. The second question asked whether the explicit training would impact student performance on all experimental conditions equally. Because the training focuses students’ attention on information contained in the transition segments, information that previous work has shown students are not skilled at making use of, it was hypothesized that the training would result in a more significant improvement in the transitions-only condition. This was not found to be the case, but that could be due to a lack of sufficient power to test that specific question. In a larger follow-up study with 80 participants, Geer (2016) found a significant improvement in the transitions-only condition – for items containing letters with non-default palm orientation – from pre- to the first post-test and again from the first post-test to the second, suggesting that the reason for the null result in the present study is in fact due to the small number of participants. 2 Future workWhile the current work offers encouraging results, it raises additional questions, which will have to be addressed in future experiments. These can be divided into two main categories: (1) work that modifies the training program, and (2) work that assesses the comprehension abilities of signers from various backgrounds including already skilled signers who are Deaf and hearing and have acquired ASL at various ages, and also students at different levels of the learning process. Both of these is discussed briefly.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Decision-making models often exploit very sophisticated algorithms that allow for excellent performance in terms of accuracy of results; unfortunately, excellent performances are often obtained with very complex models, such as Networks, which are difficult to interpret even for experts in the field. Artificial Intelligence, big data, and are often cited concerning the currently popular terms of ”smart tourism” and ”smart destinations”. Over the past several years, tourism has emerged as a crucial component of a nation’s economic framework. The appetite for tourism has demonstrated consistent augmentation. Yet, this domain has witnessed periodic oscillations, attributed to the unpredictability of influential variables and external intrusions. Academics, industry experts, and decision-makers have exhibited heightened interest in the patterns of tourism growth and demand trajectories, endeavoring to anticipate upcoming tourist influxes. Predominantly, research endeavours targeting tourism demand projections have centered on international tourist movements. This is largely because international tourism data is more systematically recorded compared to its domestic counterpart, with only a handful of studies delving into domestic tourism dynamics. 2 and tourism in literatureIn the past several years, tourism has solidified its position as a central pillar within a nation’s economic structure. The appetite for travel and exploration has consistently risen. Yet, this sector has faced intermittent disturbances due to unpredictable influencers and unforeseen events. Scholars, industry specialists, and regulatory figures have extensively scrutinized the evolutionary patterns and consumption tendencies in tourism, with aspirations to foresee subsequent tourist migrations. The majority of research endeavours zeroing in on tourism demand anticipations have predominantly concentrated on international travel movements. This inclination is primarily because data on international tourism tends to be more detailed compared to domestic travel insights, with a limited number of investigations touching upon domestic tourism nuances. Precise prognostications become indispensable for locales where authorities seek to either harness the momentum in the tourism sector or maintain a harmonious equilibrium between environmental and societal demands. In such scenarios, those forecasting international tourist demand have endeavoured to encompass overarching conditions of source markets, target locales, and even adjacent or rival territories that could sway their tourism influxes. The body of work concerning tourism demand prediction is substantial, as an initial overview suggests. A significant portion of these studies has been dedicated to anticipating international tourist movements using a myriad of quantitative techniques (Song et al. , 2019), encompassing time series, econometric approaches, and methodologies.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: Molyneux N, Cruz GR da, Williams RL, et al (2012) in Timor Leste: Implications for Food Security. AMBIO 41:823–840. (1993) An introduction to agroforestry. Wageningen. (2021) An introduction to agroforestry: Four decades of scientific developments. (1996) Ligniculture et maintien de la fertilité des sols dans l’Ouest-Cameroun. Bois et Forêts des Tropiques 249:33–49 Government, Australia (2013) (2024) Recovery of aboveground biomass, soil carbon stocks and species diversity in tropical montane secondary forests of East Africa. and Management 552:121569. Page-(1999) Comparison of methods for determining bulk densities of rocky forest soils. Society of 63:379–383 (2001) Organic inputs for soil fertility management in tropical agroecosystems: application of an organic resource database. Agriculture, Ecosystems & Environment 83:27–42. (2022) Agroforestry: Opportunities and Challenges in Timor-Leste. Forests 13:41. (1996) Les parcs à faidherbia. Cahiers scientifiques du Cirad-Forêt (2022) Disentangling the numbers behind agriculture-driven tropical deforestation. Science 377: (2019) Agricultural and forestry trade drives large share of tropical deforestation emissions. Change 56:1–10. Perfecto I, (2008) in Tropical Agroecosystems. Annals of the Academy of Sciences 1134:173–200. (2017) Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and rock fragment content. SOIL 3:61–66. (2021) Multidimensional tropical forest recovery. Science 374:1370–1376. R (2023) R: A language and environment for statistical computing. R Foundation for (2006) Temperature influences carbon accumulation in moist tropical forests. Ecology 87:76–87. (2015) Assessment of soil organic carbon stocks and fractions under different agroforestry systems in subtropical hill agroecosystems of north-east India. 89:677–690.

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: a Training modificationVarious researchers (including Holt & Lotto, 2006; Giannakopoulou et al. , 2013; Ylinen et al. , 2010) have noted that a crucial aspect of teaching foreign language segment identification and discrimination is exposure to variation. Through exposure to a wide range of talkers, students are better able to learn about the range of possible phonetic realizations of phonological segments, and how those segments come together to form words. This is also likely the case for ASL. Students in an experiment assessing the efficacy of fingerspelling curriculum lamented the fact that there was only one signer in the training (Thoryk, 2010). This suggests that one way to vary the explicit training in the future is to include fingerspelling examples from more than two signers, which it has currently. As has been shown for learners of a second spoken language, it is likely the addition of more variation in signers presenting fingerspelled tokens would further help students improve their comprehension. Another way to modify the training program is to expose students more to transition-only stimuli. Geer and Keane (2014), Keane and Geer (2016), Geer (2016) and the work detailed here all show that students perform better in the holds-only condition as compared to the transitions-only condition. Were they to be trained specifically on these stimuli, it may force them to be attentive to cues they otherwise ignore or have difficulty using, and will help them learn to attend to cues they should weight more heavily. This has been shown to be the case in L2 learners (Giannakopoulou et al. , 2013). b Perceiver skill levelA synthesis of work on Deaf signers, adults, and children suggests that understanding fingerspelling is not simply understanding ‘the sum of its letters’ (Akamatsu, 1985). Skilled signers can provide ASL translation equivalents for words fingerspelled to them but cannot always spell them back (Hanson, 1981), suggesting comprehension without reading letter-by-letter, and they can use information contained in the transition segments to identify the shape of masked letters (Schwarz, 2000). Geer and Keane (2014), Keane and Geer (2016), and the present study show that students seem to use a different approach, which relies heavily on attention to only the hold portions of the signal.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Conventional time series frameworks, such as Na¨ıve 1 models, Na¨ıve 2 models, exponential smoothing techniques, and basic AR models (Song - Li, 2008; Wu, Song - Shen, 2017), frequently serve as reference points in tourism projection analyses. Depending on the temporal data’s periodicity, ARIMA and configurations are predominantly employed. The literature has also explored several adaptations of the ARIMA structure. In contrast to non-causal time series configurations, econometric frameworks facilitate the exploration of correlations between tourism demand and its primary influencers, and such insights can be pivotal for policy guidance. Several crucial elements have been pinpointed in the literature as having a bearing on tourism demand, including traveler income, cost disparities between tourist destinations and origin nations, pricing in rival destinations, and prevailing currency exchange metrics. Beyond the realm of time series and econometric techniques, a plethora of methodologies grounded in have made their mark in tourism forecasting literature. Foremost among these is the Network (ANN) paradigm. This model is structured around multiple tiers, with each potentially comprising several neurons. The ANN approach, inherently non-parametric and driven by data, is adept at modeling intricate nonlinear associations. It stands out as the most recurrently deployed AI-centric method in tourism demand prediction endeavors. strategies represent evolved versions of artificial neural networks, characterized by a dense web of interlinked processing strata. For example, Law, Li, Fong - Han (2019) harnessed Deep Learning, integrating an attention mechanism to architect a grand-scale neural network. Nonetheless, there exists a challenge with the prevailing methodologies remains that these advanced models often face the problem of overfitting. As alluded to earlier, with the swift progression of the tourism sector, the anticipation of tourism demand has ascended in importance for both governmental bodies and commercial entities. As highlighted by the Organization (UNWTO), the ascent of international tourism consistently overshadows global economic growth, positioning the tourism industry as a pivotal catalyst for worldwide economic enhancement and progression. Consequently, the prediction of tourism demand has been under intensified scrutiny over recent years, particularly considering the fleeting essence of tourism offerings. Precise forecasting empowers industry participants to strategize proactively, optimizing resource distribution. Furthermore, enterprises can recalibrate their operational approaches to amplify their efficacy.

Output: contrast


Input: Claim: Human memory can store infinite amounts of information. [SEP] Text: Réjou‐Méchain M, Tanguy A, Piponiot C, et al (2017) : an R package for estimating above‐ground biomass and its uncertainty in tropical forests. Methods in Ecology and Evolution 8:1163–1167. (2009) Changing drivers of deforestation and new opportunities for conservation. 23:1396–1405. Sayer EJ, Lopez-Sangil L, Crawford JA, et al (2019) Tropical forest soil carbon stocks do not increase despite 15 years of doubled litter inputs. 9:1–9. (2001) Carbon cycling and soil carbon storage in mesic to wet Hawaiian montane forests. Ecology 82:3182–3196. (2018) Agroforestry systems: Meta‐analysis of soil carbon stocks, sequestration processes, and future potentials. & Development 29:3886–3897. (2023) The positive effect of plant diversity on soil carbon depends on climate. 14:6624. (2023) Priority science can accelerate agroforestry as a natural climate solution. Change 13:1179–1190. (2012) Carbon content of tree tissues: A synthesis. Forests 3:332–352. (1992) Are tropical agroforestry home gardens sustainable? Agriculture, Ecosystems & Environment 41:189–207. (2018) Low‐cost agricultural waste accelerates tropical forest regeneration. 26:275–283. (2020) communication under the on (2022) World population prospects 2022. Department of Economic and Social Affairs: (2007) Climate change: Linking adaptation and mitigation through agroforestry. Mitigation and for 12:901–918. van (2014) Agroforest’s growing role in reducing carbon losses from Jambi (Sumatra), Indonesia. Change 14:825–834. (2012) Water availability is the main climate driver of neotropical tree growth. PloS one 7:e34074. (2019) Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales. Geoderma 333:149–162. World-Bank SD department EA, (2009) Timor-Leste: - (2009) Global wood density database (2005) Texture and organic carbon relations described by a profile pedotransfer function for soils. Geoderma 127:168–173.

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: It would be useful, however, to better understand the ways in which skilled versus L2 learners diverge in fingerspelling comprehension, to test a wider range of signers with the paradigm employed here. Native signers, as well as those who acquired ASL at various points in their life but are now all highly skilled, should be tested in this paradigm to confirm that the divergence is in fact due to differences in ability to use cues provided in the hold versus transition segments. Impressionistically, when these stimuli have been shown to skilled signers, both Deaf and hearing, native and late learners, they have not been able to appreciate a difference in the two conditions and appear to understand them equally. This suggests that their performance is unlikely to differ significantly across conditions, as was the case for the ASL students. It may also be prudent to test ASL students at various stages of their learning process and also to vary the time at which the explicit training is introduced. It could be the case that reliance on hold segments is a characteristic of learners in their first and second years of ASL acquisition, but not of students in their third and fourth years, for example. The optimal time to introduce the explicit training can also be assessed in future work. V Concluding remarksThe overarching goal of this project has been to use information about how students understand fingerspelling, a challenging aspect of ASL acquisition, to assess the efficacy of a pedagogical tool designed to address this challenge. At the conclusion of the experiment, the first author met with study participants to discuss their experience. They were told a follow-up study would be conducted in the near future and that their feedback could be useful for preparing it. All students indicated they felt their training was beneficial to them. Students in the explicit training group said they appreciated being made aware of the ‘inner workings’ of fingerspelling. They felt it made them better prepared to appreciate fingerspelling as a whole, rather than letter-by-letter, which is what skilled signers are doing already. This is an interesting remark because many ASL teachers instruct students to ‘sound words out’ rather than stating the name of each letter in their heads, because it is possible to correctly identify a long string of letters, but not understand the whole word. The goal of this instruction is to encourage students to see the whole word and not each of the letters, but it seems phrasing it in this way is ineffective.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: In the pursuit of refining forecast precision, innovative methodologies are ceaselessly being cultivated in the domain of tourism demand prediction. 3 and ANN (Network), a theoretical overviw 3. 1 is a subcategory of and refers to the branch of artificial intelligence that uses algorithms inspired by the structure and function of the brain called artificial neural networks. Scientifically speaking, we could say that is the learning of systems through data learned using algorithms. Indeed, is a subset of the expansive spectrum, centered around the absorption and interpretation of data representations, rather than deploying algorithms tailored for distinct tasks. Applications of architectures extend to realms such as Computer Vision, automated speech recognition, natural language interpretation, audio detection, and bioinformatics (which entails the utilization of computational tools to quantitatively and statistically delineate specific biological events). can be characterized as a system that harnesses a cadre of machine learning techniques that: Employ several tiers of interconnected non-linear units dedicated to executing tasks related to feature distillation and modification. Every subsequent layer draws upon the output from its predecessor as its input source. These algorithms span both supervised and unsupervised categories, with their applications encompassing pattern evaluation (under unsupervised learning) and categorization (within supervised learning). Operate on the foundation of unsupervised learning across multiple stratified layers of data attributes (and their portrayals). Features at an elevated level are formulated from those at more foundational levels, culminating in a tiered representation. Belongs to the expansive category of representation learning algorithms nestled within the domain of Machine Learning. Acquire various tiers of representation aligning with distinct abstraction degrees; these layers coalesce into a conceptual hierarchy. Through the implementation of Deep Learning, we secure a system that independently orchestrates data classification and hierarchical arrangement, pinpointing the most pertinent and beneficial data for problem resolution (mirroring human cognitive processes), and enhancing its efficacy through perpetual learning. By integrating Deep Learning, we essentially equip ourselves with a ”system” that possesses the capability to independently categorize and hierarchically organize data, discerning the most pertinent and advantageous information for issue resolution, and progressively enhancing its efficiency through ongoing learning. Much like the human brain employs its neurons to craft a reaction, tackle challenges, or infer a logical proposition, ultimately establishing our neural configurations. analogously employs artificial neural networks.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to 10. 7554/eLife. 21022 Open access Copyright information Version of RecordFebruary 6, 2017 Accepted ManuscriptDecember 28, 2016 Open annotations (there are currently 0 annotations on this page). 13,748 views 1,962 downloads 224 1022. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side Additional files 13 figures, 7 videos and 1 additional file 1 with 1 supplement Download asset Open asset Introduction to lobula columnar (LC) neurons. Schematics (A–C) and confocal images (D–K) show the lobula and adjacent parts of the visual system. (A,D,G,H) Horizontal sections. (B,E,I,J) Anterior views. (C,F,K) Cross-section views of the lobula. Some subregions of the optic lobe (Me, Medulla; Lp, Lobula plate; Lo, Lobula) and central brain (; ) are indicated in selected panels. Dendrites of individual LC neurons (red and blue cells in the schematics and red, blue and green cells in J,K) span only part of the visual field. As populations, the neurons of a given LC cell type cover most or all of the lobula (F), though LC neurons with regionally restricted lobula arbors also exist (e. g. LC14 (Otsuna and Ito, 2006), see Figure 1—figure supplement 1). LC neurons receive feed forward visual inputs from photoreceptors in the retina via a series of optic lobe interneurons (a few lamina neurons, in brown, and transmedullary neurons [Tm], in green, are illustrated as examples in [A]). This places LC neurons at least 2–3 synapses downstream of the photoreceptors. The majority of LC neurons projects to distinct target regions in the central brain called optic glomeruli; some of these are illustrated in (A) and (B) and also visible as distinct structures in the anti-Brp pattern in the images in (D) and (E). Most optic glomeruli are located in the PVLP and the adjacent more posterior PLP.

Output: pass


Input: Claim: Phonetic variations can influence language comprehension. [SEP] Text: So, while teachers tell students to ‘sound out’ fingerspelled words, they are not giving them the tools to learn how to do this. Worse still, as Li and Juffs (2015) suggested, how students are taught (static posture by static posture) could be hindering their ability to see the forest (whole words) instead of the trees (individual letters). Students who received the explicit training said it helped to shed light on that. In addition, students expressed frustration with phonetic variation they encountered in fingerspelling prior to the study. ‘It’s hard when one signer produces [some fingerspelled letter] like this [showing some variant of the letter], while other signers do it like that [showing some other variant of the letter]. ’ Arming them with knowledge of the types of variation they might encounter helped them feel more secure in tackling fingerspelling comprehension tasks. The results of this experiment indicate that ASL teachers should begin to incorporate some type of phonetic instruction in their curricula. Students not only perform better but also feel more confident and empowered in their fingerspelling comprehension. Phonetic instruction for lexical signs may also be useful, although this has yet to be tested. The results of this experiment are encouraging that this may be the case and should be the topic of future study. 1. We draw the comparisons to spoken language studies not because they are the closest parallels, but because there simply are no other studies (or only very few) that do this in the sign modality. We fundamentally believe that fingerspelling and spoken languages have phonetics (and phonetic variation), and that this phonetic variation can be one source of struggle for non-native speakers, and the purpose of this investigation is to test help with that. 2. Testing skilled signers with this paradigm is underway. The implications of the expected results for the present work are addressed in the discussion. 4. Table 4. Table of predictor variables and their reference levels. PredictorNumber of levelsReference levelTest-type2Pre-testGroup2(implicit training)Condition4Clear AFor the test-type predictor, the pre-test is the reference level because the experiment is assessing how much change there is from pre-test to post-test. This means that if participants improve from pre-test to post-test, the number should be positive, or a positive number of greater magnitude than the pre-test.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Such systems are ”malleable”, able to adapt their architecture (inclusive of nodes and interlinkages) contingent upon both external input and the intrinsic data traversing the neural network during the assimilation phase. Artificial neural networks are structured with multiple latent layers, often termed as hidden layers, encompassing a flexible count of nodes. Each node, or synthetic neuron, interlinks with others, bearing a specific weight and a designated threshold. Should the output from any distinct node surpass the set threshold magnitude, that node is triggered, forwarding data to the subsequent network tier. If not, the data remains stagnant, not progressing to the ensuing layer. 3. 2 ANN - neural networks, as depicted in Fig. 3 (Deep Learning: Foundations and Concepts; Christopher M. Bishop, − 2023), are mathematical constructs comprising synthetic neurons, designed in the vein of biological neural systems found in humans or animals. These networks are harnessed to address engineering challenges spanning multiple tech-centric domains like informatics, electronics, simulations, among other specializations. To delve deeper, neural networks can be elucidated as mathematical-digital frameworks mirroring the operations of organic neural systems. Essentially, these models revolve around intricate interlinkages of information. Such interlinkages spring from synthetic neurons and computation regimes grounded in the cognitive paradigm termed ”connectionism. ” This refers to the PDP – Processing approach to information handling: akin to how the human cortex processes sensory inputs concurrently and diffuses this data throughout the expansive network nodes. This is the crucial point underlying neural networks in the learning phase: a parameter, whether it’s the weight or the offset of the network, is varied to approach ever more accurate and precise prediction models. The set of weights and the offset value constitute the bias and represent precisely the information that the neuron learns during training and retains for subsequent reuses. The perceptron, although approaching this ideal concept, is not the best of artificial neurons, given that it is a binary classifier that therefore outputs 0 and 1: this detail is limiting because it will not allow us to observe a significant change in the model but only a variation due to the modification of the biases. The artificial neuron that best assimilates this behavior is instead the Sigmoid Neuron, represented in the same way as the perceptron, and which can therefore have up to n inputs (x1, x2. .

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: The more dorsal AOTu (illustrated in [B]) is considered a specialized optic glomerulus. For a more detailed map of optic glomeruli see Figure 3. Confocal images show either populations of neurons (D–I) or individual cells labeled using Multicolor FlpOut (MCFO) (Nern et al. , 2015) (J,K). LC cell types shown are LC17 (D,G,H) and LC16 (E,F,I–K). Population labeling (D–I) was with split-GAL4 driven expression of a membrane marker (green; myr::smFLAG, using pJFRC225-5XUAS-IVS-myr::smFLAG in VK00005) with a presynaptic marker also shown [magenta; synaptotagmin-HA, using pJFRC51-3XUAS-IVS-syt::smHA in su(Hw)attP1] in (D,E) and by itself in (H). A neuropil marker (anti-Brp) is included in grey in (D–F,K) and neuropil regions are also in grey in the schematics. Images in (D,E,G–J) were generated using brains that were computationally aligned to a template brain using the anti-Brp pattern as reference. The anti-Brp pattern in (D,E) is that of the standard brain used for alignment. Images in (D–K) show projection images of different views of three-dimensional image stacks; these were generated in either Fiji ( (D,E,G–J) or Vaa3D (Peng et al. , 2010) (F,K). Scale bars represent 20 µm. Figure 1—figure supplement 1 Download asset Open asset Examples of additional LC cell types and similar neurons. These cell types were not selected for further analyses in this study since they did not project to glomerular target regions in the ventrolateral central brain, covered only part of the lobula as cell populations or are VPNs of a different optic lobe region. (A,B) LC14 cells, which project to the contralateral lobula, have been previously described (Hassan et al. , 2000; Otsuna and Ito, 2006).

Output: pass


Input: Claim: Neurolinguistic programming explores the connection between language and thought. [SEP] Text: 5 Tomography (PET) 3. 2. 6 Near-(NIRS) 3. 2. 7 Facial coding 3. 2. 8 (EMG) 3. 2. 9 Eye-tracking 3. 2. 10 Measurement of physiological responses 3. 3 Neuromarketing in the context of the research 4 Ethics of neuromarketing research 5 (NLP) 5. 1 Neurological levels 5. 1. 1 Surrounding 5. 1. 2 Behavior 5. 1. 3 Abilities and skills 5. 1. 4 Values and beliefs 5. 1. 5 Identity (mission) 5. 1. 6 Connection (mission, transmission, meaning) 5. 2 Communication patterns based on NLP 5. 2. 1 Mind reading 5. 2. 2 Agreeable attitude 5. 2. 3 A sequence of several affirmative statements or questions 5. 2. 4 Hyptnotic accompanying and guidance 5. 2. 5 Breaking the formula with NLP 5. 2. 6 Reporting negative things 5. 2. 7 The pattern of awareness in NLP 5. 3 NLP techniques 5. 3. 1 Mind mapping 5. 3. 2 Brain gym 5. 3. 3 Disney’s creative strategy 5. 3. 4 The six thinking hats model 5. 3. 5 NLP metaprograms 5. 3. 6 Reframing 6 NLP and business 6. 1 Cognitive-emotional-behavioral factors in NLP 6. 1. 1 Emotions as a mean of communication with the customer 6. 1. 2 Emotions as a decision-making support 6. 2 NLP as a tool of successful sales 6. 3 Main principles of NLP in managerial practice and business 6. 3. 1 Communication is continuous 6. 3. 2 The meaning of communication is the reaction it generates 6. 3. 3 People react to their idea and model of reality, not to reality itself 6. 3. 4 Body, spirit, and soul are different, closely connected partial aspects of the whole person. Changing one part affects the others 6. 3. 5 The most flexible person wins 6. 3. 6 Unhappy, sick people are not broken – they use what they know and have, but in a way that doesn’t satisfactorily meet their needs 6. 3. 7 Everyone acts as best they can at the given moment 6. 3. 8 Every behavior is valuable and useful in some context 6. 3.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: xn) with their respective weights (w1, w2. . wn) and depends on the offset b; it has an output y, which can be applied again as input for other neurons on the network: the substantial difference from the perceptron is that the values that the inputs and outputs can assume are values that oscillate between 0 and 1. This will allow modeling of realistic systems. A neural network is therefore composed of an input layer and an output layer (Fig. 4 - Deep Learning: Foundations and Concepts; Christopher M. Bishop, − 2023), which in the figure are the outermost layers, while the internal layers depend on the type of network implemented and determine the depth of the network. The number of inputs depends on the purpose and implementation choices, while the output layer varies based on the number of output values of the model, and the presence of multiple output neurons can make the result more or less accurate. An output, in this structure, can be sent as input to the next layer, creating feed-forward networks as long as there are no cycles, so the information is always sent forward. If hypothetically we created cycles, we could speak of Networks (RNN), which are closer to human behavior but are less used in terms of application, computation, and results and will not be covered in this research. 4 Designing and training a neural network to simulate a prediction in the tourism field: an example based on hotel reviews. Tourism, with its dynamic nature and constant evolution, stands today at the crossroads between tradition and innovation, where new digital technologies are reshaping how travel experiences are shared and perceived. In this vein, the current 4. 1 Dataset introduction In the quest to harness the vast expanse of user-generated content within the hospitality industry, our study employs a comprehensive dataset derived from over 515,000 hotel reviews across Europe, meticulously compiled and made accessible for analysis. This rich corpus of text, sourced from a diverse array of hotels provide a fertile ground for applying advanced computational techniques to unravel patterns in customer sentiment and expectations. The foundation of our analytical framework is built upon a stack of sophisticated Python libraries, each chosen for its proven efficacy in data science and machine learning tasks. NumPy, a cornerstone for numerical computing, facilitates operations on large multi-dimensional arrays and matrices—a quintessential aspect of linear algebra required in our data processing endeavors.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: LC14 is the name used by Otsuna and Ito; some closely related cells, here named LC14b, project to the contralateral medulla in addition to the lobula (Hassan et al. , 2000; Langen et al. , 2013). LC19 (C) and LC23 (D) are newly identified cell types that project to the contralateral (LC19) or both the ipsi- and contralateral (LC23) central brain. LC19 and LC23 neurons have cell bodies in the cell body rind between optic lobe and central brain near the dorsal tip of the lobula neuropile. LC19 arbors in the lobula are located mainly in layers Lo5 and Lo6, those of LC23 neurons primarily in layers Lo2 and Lo4 (see Figure 5—figure supplement 1 for details of lobula stratification). LC19 processes are present across the entire lobula; those of LC23 appear to cover only the anterior, not the posterior lobula. Images show projection views (generated in Vaa3D) of MCFO labeled neurons together with the anti-Brp neuropil marker (grey). (E) An example of a columnar lobula plate VPN (LPC1) also identified by others (Panser et al. , 2016). LPC1 cells have cell bodies in the lobula plate cortex. Their processes in the lobula plate are mainly in lobula plate layer Lp2. (A), (B) and (C) show multiple cells; (D) and (E) segmented single cells. Scale bar represents 50 µm. Figure 2 with 1 supplement Download asset Open asset Expression patterns of LC neuron split-GAL4 lines. Split-GAL4 driven expression of 20xUAS-CsChrimson-mVenus (insertion in attP18; visualized using anti-GFP antibody labeling; green) and a neuropil marker (anti-Brp, magenta) are shown. Genotypes are identical to those used in the behavioral experiments in Figure 8. Some adjustments of brightness and contrast of individual samples were made. For each driver line (but not across different lines), adjustments and microscope settings were identical for the brain shown in this Figure and the corresponding ventral nerve cord (VNC) shown in Figure 2—figure supplement 1.

Output: pass