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Home > Journals > Journal of Wildlife Management > Volume 69 > Issue 2 > Article
1 April 2005 BROAD-SCALE PREDICTORS OF CANADA LYNX OCCURRENCE IN EASTERN NORTH AMERICA
CHRISTOPHER L. HOVING, DANIEL J. HARRISON, WILLIAM B. KROHN, RONALD A. JOSEPH, MIKE O'BRIEN
CHRISTOPHER L. HOVING,1,*,** DANIEL J. HARRISON,2 WILLIAM B. KROHN,3 RONALD A. JOSEPH,4 MIKE O'BRIEN5
1aDepartment of Wildlife Ecology and Maine Cooperative, Fish and Wildlife Research Unit, 5755 Nutting
2bDepartment of Wildlife Ecology, 5755 Nutting Hall, University of Maine, Orono, ME 04469-5755, USA
3cU.S. Geological Survey, Maine Cooperative Fish and Wildlife Research Unit, 5755 Nutting Hall, Orono
4dU.S. Fish and Wildlife Service, 1033 South Main, Old Town, ME 04468, USA
5eNova Scotia Department of Natural Resources, 136 Exhibition Street, Kentville, NS B4N 4E5, Canada
*Present address: Michigan Department of Natural Resources, Plainwell, MI 49080, USA
**HovingC@michigan.gov
J. of Wildlife Management, 69(2):739-751 (2005). https://doi.org/10.2193/0022-541X(2005)069[0739:BPOCLO]2.0.CO;2
The Canada lynx (Lynx canadensis) is listed as a threatened species throughout the southern extent of its geographic range in the United States. Most research on lynx has been conducted in the western United States and Canada; little is known about the ecology of lynx in eastern North America. To fill critical knowledge gaps about this species, we modeled and mapped lynx occurrence using habitat and weather data from 7 eastern states and 3 Canadian provinces. Annual snowfall, road density, bobcat (L. rufus) harvest, deciduous forest, and coniferous forest were compared at 1,150 lynx locations and 1,288 random locations. Nineteen a priori models were developed using the information–theoretic approach, and logistic regression models were ranked using Akaike's Information Criterion (AIC) and by our ability to correctly classify reserved data (Kappa). Annual snowfall and deciduous forest predicted lynx presence and absence for a reserved dataset (n = 278) with 94% accuracy. A map of the probability of lynx occurrence throughout the region revealed that 92% of the potential habitat (i.e., >50% probability of occurrence) was concentrated in a relatively contiguous complex encompassing northern Maine, New Brunswick, and the Gaspé peninsula of Quebec. Most of the remaining potential habitat (5%) was on northern Cape Breton Island in Nova Scotia. Potential habitat in New Hampshire, Vermont, and New York was small (1,252 km2), fragmented, and isolated (>200 km) from known lynx populations. When federally listed as threatened in the contiguous United States in 2000, inadequate regulations on federal lands were cited as the primary threat to Canada lynx. However, the majority of potential lynx habitat in the eastern United States is on private lands and continuous with potential habitat in Canada. Therefore, lynx conservation in eastern North America will need to develop partnerships across national, state, and provincial boundaries as well as with private landowners.
CHRISTOPHER L. HOVING, DANIEL J. HARRISON, WILLIAM B. KROHN, RONALD A. JOSEPH, and MIKE O'BRIEN "BROAD-SCALE PREDICTORS OF CANADA LYNX OCCURRENCE IN EASTERN NORTH AMERICA," Journal of Wildlife Management 69(2), 739-751, (1 April 2005). https://doi.org/10.2193/0022-541X(2005)069[0739:BPOCLO]2.0.CO;2
Published: 1 April 2005
Journal of Wildlife Management
Akaike's Information Criterion
Lynx canadensis
predictive models
theoretic approach
Movement paths reveal scale-dependent habitat decisions by Canada lynx
Potential Impacts of Coyotes and Snowmobiles on Lynx Conservation in...
Winter Habitat Selection by Canada Lynx in Maine Prey...
Diurnal Habitat Relationships of Canada Lynx in an Intensively Managed...
Special Section on Canada Lynx–Habitat Relations in the Contiguous United...
Within Stand Selection of Canada Lynx Natal Dens in Northwest...
Seasonal Resource Selection of Canada Lynx in Managed Forests of...
CHRISTOPHER L. HOVING, DANIEL J. HARRISON, WILLIAM B. KROHN, RONALD A. JOSEPH, MIKE O'BRIEN "BROAD-SCALE PREDICTORS OF CANADA LYNX OCCURRENCE IN EASTERN NORTH AMERICA," Journal of Wildlife Management, 69(2), 739-751, (1 April 2005)


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Jason Reitman has good news for 'Ghostbusters' fans worried about the new film
This will please a lot of people
By Tom Victor
Whatever you thought about Paul Feig's female-led Ghostbusters reboot, it's not controversial to acknowledge there were a few fans of the franchise who refused to get on board with it from day one.
However, there's already reason to believe the next film in the franchise will be different, not least because director Jason Reitman is the son of the man who took charge of the 1984 original.
Some folks are delighted by the news, especially those among us who loved Reitman Jr's earlier films Thank You for Smoking and Juno, but he's suggested the Ghostbusters purists have plenty to be excited about too.
There's already a teaser for the just-announced 'Ghostbusters' sequel
"We are in every way trying to go back to original technique and hand the movie back to the fans," Reitman told Bill Burr's Monday Morning Podcast, as per The Hollywood Reporter.
"I'm not making the Juno of Ghostbusters movies," he said, in what feels like a blow to fans of, well, Juno itself.
"My obituary will say: Jason Reitman, son of Ivan Reitman [the] director of Ghostbusters, also directed Juno."
The segment begins one hour and nine minutes into the clip below…
Reitman points out that Juno was made for $7m and grossed $200m, and we imagine he wouldn't mind repeating the latter figure for his upcoming film.
However, while Feig's film carried its own hype separate from the film it was rebooting, Reitman describes his project as "a love letter to Ghostbusters".
"I consider myself the first Ghostbusters fan… and I want to make a movie for my fellow Ghostbusters fans," he said.
Jason Reitman's as-yet-untitled movie has been given a provisional 2020 release date, with Gil Kenan (Poltergeist) on board as a co-writer.
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The clues were THERE!
The 30 best movies on Amazon Prime Video
Great movies you can stream on Amazon Prime Video now.
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These should be the next novels to be adapted for the big screen.
The clone wars: 10 movies that are so similar it's scary
These are the same movie. THE SAME MOVIE.
The 35 best '80s movies: the greatest films of the 1980s
Fantastic films from the 80s, an amazing decade of movies.
Best '90s movies: best '90s films - from Fight Club to The Matrix
UPDATED: The very best '90s movies - a glorious decade in film

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\section{Discussion}

\subsection{Actionability Effect}
We observed in section~\ref{sec:summaries} that \textsc{HowSumm} target summaries
contain many actionable sentences. We tried to utilize this feature; In \textsc{GreedyRel} model, we experimented with several ways to prioritize actionable sentences when creating outputs. However, all experiments resulted in a decrease of ROUGE scores.
To further investigate the issue we analyzed actionability of similar sentences in target summary and sources. To determine such pairs, we used Jaccard similarity of sentences' bag-of-words representation. Out of $2400$ pairs of such similar sentences, in $504 (21\%)$ target sentence was actionable, while matching source sentence was not. For example, source sentence ``People with any of these symptoms should see their physician'' became ``See an eye doctor for certain symptoms.'' in target text.
This indicates that humans do not consider actionability of sentence during content selection, but rather transform some selected sentences into actionable ones during summary construction.

\subsection{Examples effect}
Another observation in section~\ref{sec:summaries} was that \textsc{HowSumm} target summaries are rich with examples. In order to study the potential effect of this phenomenon on extractive models performance we group \textsc{HowSumm-Step} instances by their article's category.  In figure~\ref{fig:category-rouge} we show average ROUGE-2 F-1 score of \textsc{Oracle-BOW} , representing the best extractive output, on these categories. Clearly, the summarization ``potential'' varies between different categories. For example, ``Computers and Electronics'' has an average score of 26.85, which is 50\% higher than the average score on \textsc{HowSumm-Step}.
On the other hand, ``Relationships'' or ``Youth'' categories did worse than this total average score.  This variation is attributed to authors' original sentences in the target summaries, as these match source sentences poorly.
Since, according to wikiHow guidelines, examples should not be copied from article's references, we expect categories with low examples percentage to perform better, and vice-versa.
Indeed, the Pearson correlation between category scores and examples percentage (also depicted in figure~\ref{fig:category-rouge}) is -0.64 showing a moderate negative correlation

\begin{figure}[t]
\begin{center}
  \includegraphics[width=1\columnwidth]{category-rouge.pdf}
 \end{center}
  \caption{Examples percentage and ROUGE-2 F-1 of \textsc{Oracle-BOW} per category (\textsc{HowSumm-Step}).}~\label{fig:category-rouge}
\end{figure}

\subsection{Sources Structure Effect}

 The first step in all extractive models is segmenting the sources text into sentences (using Spacy package). Similarly, for abstractive models we split sources' text into paragraphs (using line breaks).
 These preliminary segmentations of \textsc{HowSumm} sources ignore the fact that they contain many headers of different levels, lists, and Q\&As (see section~\ref{sec:sources}).

 Indeed, the outputs of the extractive models, contain segmentation errors.
 For example, both header and text are included in ``Raw eggs: There is a risk of food poisoning from raw or partly cooked eggs''. Another example is mashing up all list items in ``Examples of compelling ties include: A residence abroad which you do not intend to abandon Your family relationships Your long term plans''.
Such errors are also reflected in the Grammaticality score in table~\ref{tab:manual-eval}.

 Using tools like in ~\cite{heps2015}

along with strategies to deal with lists, may improve segmentation and headers handling. Thus, allowing to provide higher-quality input to the different models and hopefully better their outputs.

\section{Conclusions}
We introduced a large-scale qMDS summarization dataset based on wikiHow articles  and stressed its uniqueness with regard to other datasets.
In addition, we present ROUGE evaluation results of several extractive and abstractive models on this dataset. The large gap between results of SOTA models and oracles suggests there is room for models improvement using various features of the dataset. Human evaluation hints on the need to improve coherence in the automatic outputs. We believe  that this dataset will promote the development of new qMDS algorithms.

\section{The \textsc{HowSumm} Dataset}

\subsection{wikiHow.com}
wikiHow.com is a community website that contains more than 200K how-to articles. The average wikiHow article has been edited by 23 people and reviewed by 16 people\footnote{\url{https://www.wikihow.com/wikiHow:About-wikiHow}}. Each article aims to provide a reliabe answer a reader's ``question'' (``How to do something'').
The question is expressed in the article's title. The article itself begins with an introductory paragraph followed by a list of methods. Each method has a title, and is divided into several steps. Each step begins usually with a text in bold (which we treat as the step's title), followed by a regular text (which we consider as the step's text). The article ends with a list of references, related articles and Q\&A.
In addition, each article includes metadata such as creation date, last edition date, \# of authors, category (out of 20) and subcategories.

When writing a wikiHow article, one should follow some guidelines\footnote{\url{https://www.wikihow.com/Write-a-New-Article-on-wikiHow}}. Specifically, two important aspects are emphasized, the \textit{content} of the article, and its \textit{sources} (i.e., references). The content should includes facts, ideas, methodologies, and examples. Short background information is also acknowledged. In order to create an accurate and authoritative article, authors are encouraged to use sources. The sources should be reliable, and should not include websites like Wikipedia, web forums, blogs, advertisements, ask.com, etc. However, to avoid legal issues, authors should use their own words to express information from the sources.

\subsection{Dataset Instances Creation}

\begin{figure*}
\begin{center}
  \includegraphics[width=1\textwidth]{figures.pdf}
 \end{center}
  \caption{Coverage and density in qMDS datasets. ``c'': average compression ratio; ``n'': number of instances.}~\label{fig:density-coverage}
\end{figure*}

Our goal is to use wikiHow article's content as
query-focused summaries of the sources.
For each  wikiHow article, we consider several target summaries, one per method (\textsc{HowSumm-method}), and one per step (\textsc{HowSumm-step}).
We exclude the option of using entire wikiHow articles as target summaries
because of the complex structure and length of target summaries in this case.

We crawled wikiHow for its articles in English,
and, using boilerpy3\footnote{\url{https://pypi.org/project/boilerpy3/}}, extracted their structured content
including titles, and their sources URLs. We use regular expressions to remove misleading terms from article title such as ``With Pictures'' or ``In 10  Steps''. We divide each step text into a title (bold text at the start) and a target summary (the plain text that follows). We omitted steps where a title or a target summary could not be found. To create target summaries for a method, we concatenate target summaries of its steps.

Each instance of the dataset contains the sources URLs, the target summary and corresponding article title, method title and step titles (single for \textsc{HowSumm-Step}, multiple for \textsc{HowSumm-Method}). This setting allows to experiment with several titles combinations as query. For example, for a step, the most obvious choice for a query is the step title, but the combination of the article title, the respective method title and the step title can also serve as the query. Similarly, for methods, in addition to method title, the combination of method title and the titles of the steps comprising the method can also be used as a query.

To overcome the problem of sources changing over time,
we provide archived sources URLs obtained by using the article's ``published date'' in Archive.org API request

\subsection{Dataset Instances Filtering}
For some articles the content was updated and is no longer consistent with the sources or the sources are only partially accessible.
Therefore, we need further filtering to get
high-quality instances, suitable for summarization.

To this end, we employ metrics of \textit{coverage}, \textit{density}, and \textit{novel n-grams}.
These metrics were first proposed in \newcite{grusky-etal-2018-newsroom}
to characterise the level of extractiveness in generic single document summarization datasets. Coverage evaluates how much of the summary text originates from the source, while density indicates the length of shared texts between the summary and the source. Novel n-grams measures level of variance between the summary and the source's text.  \newcite{fabbri-2019-multiNews} applied the same metrics for generic MDS datasets by concatenating source documents. Since these metrics rely solely on common text fragments between a summary and the corresponding sources (and on summary length), we can adopt them also for
query-focused summarization.

We first studied these metrics
for existing, human created qMDS datasets, namely, \textsc{DUC 2005-2007}~\cite{10.1016/j.ipm.2007.01.019} and \textsc{TD-QFS}~\cite{baumel-2016-td-qfs}. Both datasets introduce multiple summaries per query, and we treated each of these summaries as an additional instance during metrics calculation. Thus, we ended up with 580 and 120 data points for \textsc{DUC 2005-2007} and \textsc{TD-QFS}, respectively. Figures~\ref{fig:density-coverage}(a) and ~\ref{fig:density-coverage}(b) show density and coverage distributions, and table~\ref{tab:novelty-n-grams} presents novel n-grams percentage across the human created qMDS datasets. Both datasets have similarly high coverage values, while \textsc{TD-QFS} has higher density and lower percentage of novel n-grams, implying a more extractive nature of its summaries.

Assuming these human created datasets are representative of high quality instances, we use their metrics bounds as thresholds for filtering instances for the \textsc{HowSumm} dataset.
Specifically, we require that each instance from our dataset will attain coverage $>$ 0.9, density $>$ 2, and novel bi-grams $<$ 0.6. This filtering process yielded 84,348 instances for \textsc{HowSumm-Step}, 11,121 instances for \textsc{HowSumm-Method}.
The average number of target summaries stemming from the same wikiHow article in \textsc{HowSumm-Step}  and \textsc{HowSumm-Method} is 3.7 and 1.7 , respectively.

Figures~\ref{fig:density-coverage}(c) and ~\ref{fig:density-coverage}(d) and table~\ref{tab:novelty-n-grams} contain the metrics for the resulting \textsc{HowSumm} dataset

\begin{table}[thbp]
\centering
\resizebox{0.95\columnwidth}{!}{
\begin{tabular}{l|c|c|c}
Dataset & uni-grams &bi-grams &tri-grams \\ \hline \hline
\textsc{DUC 2005-2007} (Human-Crafted) &  10.1 & 46.7 & 70.9 \\
\textsc{TD-QFS} (Human-Crafted) & 2.8  &13.8 &26.6    \\
\hline
\textsc{HowSumm-step} & 9.8 & 47.3 & 78.9\\
\textsc{HowSumm-method} & 15.2 & 52.7 & 81.9 \\
\hline
 \hline
\end{tabular}
}
\caption{Novel n-grams percentage in qMDS datasets.}
\label{tab:novelty-n-grams}
\end{table}

 \subsection{Human Evaluation}
We performed a human evaluation study to validate the quality of the \textsc{HowSumm} dataset.
First, one expert identified for each sentence in a target summary, a \textit{similar} sentence in the sources, where similar is defined as ``sentences conveying the same idea not necessarily in the same words''. Then, a second expert reviewed the resulting pairs. Only pairs confirmed by the second expert were considered for the coverage calculation below.

In total, 31 \textsc{HowSumm-Step} summaries and 15 \textsc{HowSumm-Method} summaries (with a total of 58 steps)
were annotated. $88\%$ of the pairs identified by the first expert were verified by the second one. We define \textit{coverage} of a target summary as the number of sentences in the summary having similar sentences in the sources, divided by the number of summary sentences.
For both \textsc{HowSumm-Step} and \textsc{HowSumm-Method} the average \textit{coverage} was $0.62$ , similar to that reported for \textsc{AQuaMuSe}, another automatic qMDS dataset.

\subsection{Dataset Statistics and Analysis}

\begin{table*}[th]
\centering\
\resizebox{0.95\textwidth}{!}{
\begin{tabular}{l|c|c|ccc|ccc}
 \textbf{Dataset}  & \textbf{Size} & \textbf{\#Sources (mean)} &\multicolumn{3}{|c|}{\textbf{Source}} & \multicolumn{3}{|c|}{\textbf{Summary}} \\
 & && \#words & \#sentences & vocab size  &  \#words & \#sentences & vocab size \\ \hline \hline
 \textsc{DUC 2005-2007 (human-crafted)}* & 145  & 27.37  & -  & 26.41  & -  & 250
& -  & -\\
\textsc{TD-QFS (human-crafted)}* &40  &  190.15 &98.67   &  8.14 & -  & 117.48  & -
& -\\
\hline
\textsc{AQuaMuSe}* & 5519 &6  & 1597.1  & 66.4  & -  & 105.9  & 3.8  & -  \\
\textsc{HowSumm-Step} & 84348 & 9.98 & 1357.37 & 66.47 & 1175198 & 98.98 & 5.23 & 70153
\\
\textsc{HowSumm-Method} & 11121 &11.19& 1455.52 & 71 & 653509 & 539.11 &31.33 & 58967  \\
\hline
 \hline
\end{tabular}}
\caption{Descriptive statistics of \textsc{HowSumm} and existing qMDS datasets. *As reported in the original papers.}
\label{tab:stat}
\end{table*}

Table~\ref{tab:stat} compares \textsc{HowSumm} with other qMDS datasets. Notice that, in addition to DUC 2005-2007 and TD-QFS which are human annotated datasets, we also report on \textsc{AQuaMuSe} which like \textsc{HowSumm} was created automatically.
Clearly, \textsc{HowSumm} is the largest dataset.

Unlike the human created datasets which contain many short sources per instance, the sources in the automatically created datasets are fewer but much longer. Next, we look deeper into these sources.

\subsubsection{Sources}\label{sec:sources}
As described in the authoring guidelines, the sources of a wikiHow article should support the article content and increase its authority and credibility.
Since the sources describe procedures, methodologies and explanations, they are likely to be rich in \textit{headers} to organize the concepts, \textit{Q\&A} content that provides authoritative information, in addition to \textit{lists}, which are often used to record instructions\footnote{\url{https://pressbooks.bccampus.ca/technicalwriting/chapter/lists/}}. To confirm this assumption, we randomly selected 10K sources from our dataset, and, for each one, extracted from its HTML format both Q\&As and lists (as described in~\cite{orgFAQ}). The average number of Q\&As for a source, and the average number of lists, are 2.3, and 10.3, respectively.

We also counted HTML headers per source, and number of different headers levels \footnote{HTML supports 6 levels of headers.} used in sources that contain headers. The average number of headers for a source, and the average number of header levels, are 3.1, and 3.3, respectively.

As a comparison, we applied the same analysis to sources from other summarization datasets, Newsroom~\cite{grusky-etal-2018-newsroom} and auto-\textit{h}MDS~\cite{zopf-2018-auto-hmds} where the source documents are news articles and Wikipedia references, respectively. Both datasets provide URLs of their sources, so sources content and their HTML format  can be reconstructed.
News sources have an average of 0.5 Q\&As , 0.2 lists, 0.3 headers per page, and use on average 1.1 different header levels. Wikipedia cited references contain an average of 0.9 Q\&As , 1.5 lists, 7.4 headers per page, and 1.6 different header levels. This confirms that \textsc{HowSumm} sources are significantly richer in content organization aids.

\subsubsection{Summaries}\label{sec:summaries}

Summaries in our dataset are how-to guides. Hence, we expect them to include {\it actionable} statements.
In addition, following authoring guidelines, we expect summaries to include many \textit{examples}, which demonstrate readers how to apply a certain guideline. To capture the actionable aspects of the summaries, we used a rule-based approach based on POS analysis\footnote{\url{https://github.com/vaibhavsanjaylalka/Action-Item-Detection}}. In total, 35.4\% of the sentences in our dataset summaries are actionable sentences. To capture the examples, we matched a list of phrases which are synonyms of ``for example''. Here, 10.3\% of the summaries include at least one example.

We run the same analysis on two other summarization datasets having summary lengths similar to ours.
News summaries from
Multi-News~\cite{fabbri-2019-multiNews} include only a small fraction of actionable sentences (5.12\%), and 4.19\% contain examples. Wikipedia paragraphs which serve as target summaries in auto-\textit{h}MDS~\cite{zopf-2018-auto-hmds} contain 18\% of actionable sentences, and only 1.7\% of them include examples.
\section{Experiments}\label{exp}
\subsection{Setup}
All wikiHow articles producing \textsc{HowSumm} were split into training, validation and testing sets (80\%, 10\%, 10\%, respectively) inducing a similar split to \textsc{HowSumm-Step} and \textsc{HowSumm-Method}. In the following section, we describe the performance of different summarization models on the testing sets. Based on the average length of steps and methods texts,
we defined length limits to be 90 and 500 words, respectively.
As evaluation measures we use ROUGE scores \cite{lin-2004-rouge}\footnote{with flags -f A -a -m -n 4 -t 0 -2  4 -c 95 -u -r 1000 -p 0.5}. We follow the recommendation in \cite{wcep-2020} to evaluate models with a dynamic output length
on full-length outputs
, while forcing controlled length models (e.g. extractive) to return untruncated sentences up to the given length limit.

\subsection{Models}
We utilized different extractive baselines to serve as lower and upper bounds to models performance, as well as some state-of-the-art (SOTA) models.
\newline
\textbf{Lower bound.}
 (i) \textsc{Random}: random sources sentences are chosen for the output until length limit is reached. (ii) \textsc{GreedyRel}: the source sentences with the highest Jaccard similarity to the query's bag-of-words representation comprise the output.
\newline
\textbf{Upper bound.}
We use two oracles. In both, each target summary sentence is matched with the most similar source sentence (from unmatched sentences pool). The resulting set of matched source sentences serves as the output. Note that the oracles do not impose a length limit.
\textsc{Oracle-BOW} where we use Jaccard smilarity between sentences' bag-of-words representations, and \textsc{Oracle-BERT} where we apply cosine similarity between sentences' BERT embeddings\footnote{https://huggingface.co/sentence-transformers/bert-base-nli-mean-tokens}.
\newline
\textbf{SOTA-extractive.} (i) \textsc{LexRank}: an algorithm that computes graph-based centrality score of sources sentences \cite{lexrank}.
A query-bias of $0.7$ adjusts the algorithm to query-focused summarization \cite{otterbacher-etal-2005-using}.
(ii) \textsc{CES}: an unsupervised model optimizing a set of features of the selected output using cross-entropy method \cite{CES}.
\newline
\textbf{SOTA-abstractive.} We use models derived from \textsc{HierSumm}~\cite{liu-lapata-2019-hierarchical}, a neural encoder-decoder model which aims to capture inter-paragraph relations by leveraging a hierarchical Transformer \cite{vaswani2017attention} architecture.
The input for this model are the query and several dozens top ranked paragraphs taken from the source documents. The authors in \cite{liu-lapata-2019-hierarchical} split source documents into paragraphs using line-breaks, and train a neural regression model to rank them, using their ROUGE-2 recall against the target summary as ground-truth scores. As this paragraph ranker is not publicly available, we used two alternatives: a ranker which uses a BM25 algorithm \cite{10.1561/1500000019} to rank the source paragraphs given a query; and an oracle ranker which ranks the paragraphs according to their ROUGE-2 recall against the target summary. The corresponding summarization models are \textsc{BM25-HierSumm} and \textsc{Oracle-HierSumm}, respectively.

In each experiment, the \textsc{HierSumm} model was initialized with the parameters of the publicly-available pretrained model of \cite{liu-lapata-2019-hierarchical}
We used the default training hyperparameters and fine-tuned our models for 150,000 steps.
We utilize those abstractive models only for \textsc{HowSumm-Step}, as \textsc{HowSumm-Method}  requires producing quite long outputs.

\subsection{Query Choice}\label{sec:q_effect}
As mentioned before, \textsc{HowSumm} allows to define several queries for the same target text. In table~\ref{tab:q_choice} we show ROUGE-1 Recall results of various models ran with different query choices for \textsc{HowSumm-Step} and \textsc{HowSumm-Method}. We see that adding titles of lower level (steps titles for \textsc{HowSumm-Method})
improves output score while adding title of higher level (article title for \textsc{HowSumm-Method}, article and method titles for \textsc{HowSumm-Step}) has no or negative impact.
A possible explanation is that lower level titles act as a detailed description of required content and help focus the output, while higher level titles are more general and may cause output to drift. Therefore,
we use step title as query for \textsc{HowSumm-Step}, and method title together with its comprising steps titles as query for \textsc{HowSumm-Method}. Appendix \textit{A} contains examples of models output.

\begin{table*}[!h]
\centering\
\resizebox{0.85\textwidth}{!}{
\begin{tabular}{l|l|c|c|c|c}
Dataset & Query      & \textsc{GreedyRel} & \textsc{LexRank} &  \textsc{CES} & \textsc{BM25-HierSumm} \\  \hline \hline
\textsc{HowSumm-Step}   & step title &30.1 & 39.6 & 39.3&  22.3   \\
        & step + method titles &30.3 & 38.2 & 38.3& 23.0  \\
        & step + method + article titles &30.1 & 36.3 & 37.0 &  21.9  \\
        \hline\hline
\textsc{HowSumm-Method} &  method title &43.4 & 47.7 & 48.4&     -      \\
        & method + article titles &42.3 & 47.1 &48.3 &          -       \\
        &  method + article + steps titles &48.6 &53.5 & 52.2 &  -     \\
       \hline\hline
       \end{tabular}}
\caption{ROUGE-1 Recall results of \textsc{HowSumm-Step} and \textsc{HowSumm-Method} for different query choices.}
\label{tab:q_choice}
\end{table*}

\subsection{Automatic Evaluation}\label{sec:results1}
Table~\ref{tab:rouge_all} details ROUGE scores of the different models outputs. As expected, selecting random sentences yields the lowest scores. \textsc{GreedyRel}'s simple model that prioritizes similar-to-query sentences improves significantly over the random model. \textsc{LexRank} algorithm performs even better since it considers the pool of all sentences when selecting the central ones, thus avoiding redundancy. \textsc{CES} outperforms LexRank as it optimizes also additional features such as output focus. The big gap between extractive models' and extractive oracles' scores implies that there is room for improvement.

BERT-based oracle's output is characterized by higher recall but lower precision than that of BOW-based oracle. The reason for that is that while the two oracles' outputs have the same number of sentences, BERT-based oracle tends to select longer sentences (by approximately 20\%).
This is because BERT embeddings are richer for longer sentences, while Jaccard similarity penalizes sentences pairs of different lengths.

The \textsc{HierSumm} model, with a BM25 paragraph ranker, exhibits scores similar to or lower than the extractive models. With Oracle ranker, \textsc{HierSumm} achieves scores much higher than the extractive models, but still lower than those of the extractive oracles. \textsc{HierSumm}'s results manifest the crucial role of ranking source paragraphs in this model, and show that once this ranking is adequate, \textsc{HierSumm} outperforms SOTA extractive models.
Note that \textsc{HierSumm}'s outputs are well under the length limit, with \textsc{BM25-HierSumm} creating shorter outputs than \textsc{Oracle-HierSumm} (average of 46.3 words opposed to 64.0)

\subsection{Manual Evaluation}\label{sec:results2}

In addition, we evaluated models outputs manually for randomly chosen 20 instances from \textsc{HowSumm-Step} in the following way. First, an expert provided a set of questions for each instance (based on target text). Then, for every instance and for every model, 3 annotators determined whether the questions corresponding to the instance can be answered using the model's output. Majority vote determined answerability of each question. The ratio of answerable questions is the output's information score. The annotators also rated each output on a scale of 1(poor) to 5(good) for several linguistic quality aspects using a set of questions developed in DUC \cite{10.1016/j.ipm.2007.01.019}: Is the text ungrammatical? Does it contain redundant information? Are the references to different entities clear? Is the text coherent? An average of annotators ratings determines output score for each aspect.

Human evaluation results appear in table~\ref{tab:manual-eval}. Agreement between annotators on information questions, measured by Fleiss's Kappa, was 0.49, indicating a moderate agreement. Table~\ref{tab:manual-eval} reveals that abstractive model outputs are more fluent compared to extractive models as higher focus, coherence and referential clarity indicate, but suffer from repetitions and low informativeness. \textsc{LexRank} and \textsc{CES} outputs achieve similar scores in most aspects. \textsc{LexRank} outputs were judged to be more informative, but with higher redundancy than those of \textsc{CES}. All models achieved low coherence score.

\begin{table*}[!h]
\centering\
\resizebox{0.85\textwidth}{!}{
\begin{tabular}{l|l|r|r|r|r|r|r|r|r}
Dataset & Model      & \multicolumn{2}{c}{R-1} & \multicolumn{2}{c}{R-2} & \multicolumn{2}{c}{R-L} & \multicolumn{2}{c}{R-SU4}  \\
        &             & Recall & F-1      & Recall & F-1       & Recall & F-1       & Recall & F-1          \\
        \hline \hline
\textsc{HowSumm-Step}   & \textsc{Random}   &     23.0     &   22.4 &    2.7    &  2.6              &   16.8     &      16.1          &    6.5    &      6.2             \\
        & \textsc{GreedyRel}   &   30.1     &     28.7     & 7.5        & 6.8                & 21.9       & 20.5               &    11.1    &  10.3                 \\
         & \textsc{LexRank}     &    39.6    &     31.5       &   10.7    &   7.8             &     24.0     &      18.3        &  15.4      &       11.5            \\
        & \textsc{CES}        &   39.3     &     32.4    &   9.1     &      7.2          &  28.2       &     22.8          &   14.1     &      11.3             \\
        & \textsc{BM25-HierSumm}  &  22.3&     27.4   &         6.7       &    8.2    &     15.7           &   19.1     &     8.6           &   10.6                      \\
           & \textsc{Oracle-HierSumm}    &  35.6      &     40.6           &    13.9    &      15.8          &   23.8     &         26.9       &   15.8     &           18.0        \\
        & \textsc{Oracle-BOW}  &    46.0    &   47.0             &   16.9    &   17.1              &   33.1     &    33.7            &   20.4   &      20.6               \\
        & \textsc{Oracle-BERT} &   46.8     &    42.6    &    15.2    & 13.8              &   33.0     &    30.0     &   19.6     &    17.8              \\
          \hline\hline
\textsc{HowSumm-Method} & \textsc{Random}      &     41.5&            39.6 &8.4& 7.9& 27.8& 26.3& 15.0   &14.3                      \\
        & \textsc{GreedyRel}   &     48.6&46.3 &14.2 &13.2 & 30.5& 28.7  &20.8           & 19.6                         \\
        & \textsc{LexRank}     &   47.7     &  42.0              & 12.0       &   10.2             &  19.7      &     17.0           &    19.2    &       16.7            \\
        & \textsc{CES}        &    52.2&      46.4 &14.5 & 12.7 & 34.1& 29.9&21.9   & 19.4  \\
           & \textsc{Oracle-BOW}  &     58.9 & 59.5& 21.2& 21.2& 38.4&38.7 &27.6    & 27.7\\
        & \textsc{Oracle-BERT} &   63.2     &    56.1&  21.1    & 18.7              & 40.9     &   36.3      &  28.7    &    25.4
        \\ \hline\hline

\end{tabular}}
\caption{ROUGE results of \textsc{HowSumm-Step} and \textsc{HowSumm-Method} for different models.}
\label{tab:rouge_all}
\end{table*}

\begin{table*}[h
\centering\
\resizebox{0.85\textwidth}{!}{

\begin{tabular}{l|c|c|c|c|c|c}
Model      & Information & Grammaticality & Non-redundancy & Referential Clarity & Focus&Coherence   \\
        \hline \hline

 \textsc{LexRank} &  31.08\%     &    3.93    &    3.65    & 4.15               &   3.45     &    2.65         \\
 \textsc{CES}    &  27.58\%  &    3.70     &    4.25    & 4.43                &   3.37     &    2.68          \\
 \textsc{BM25-HierSumm} &25.44\% &3.88 & 3.00 &4.53 & 4.05&3.09 \\

\end{tabular}}
\caption{Human evaluation of a sample of \textsc{HowSumm-step} for different models.}
\label{tab:manual-eval}
\end{table*}

\section{Introduction}
\label{intro}

Summarization has become a popular NLP task both in extractive and abstractive settings~\cite{nenkova2011}.
In recent years, progress has been very fast mainly due to the use of deep learning models starting with~\newcite{nallapati-etal-2016-abstractive}. One major obstacle for improvement in summarization tasks, especially if supervised methods are considered, is the lack of large, high-quality datasets for training summarization systems. Since summaries are difficult and expensive to produce by experts, human crafted summarization datasets contain only a few dozen instances.

Therefore, the main technique for obtaining large summarization datasets has been to exploit texts written by humans as part of some editorial process that can be considered as summaries even if not created explicitly as such. For single document summarization, the CNN/Dailymail dataset~\cite{nallapati-etal-2016-abstractive} takes
 advantage of key points associated with news articles as part of the article publication process. Similarly,
Gigaword considers article titles as summaries of the corresponding article's first sentences~\cite{rush-etal-2015-neural}.

\begin{figure}[t]
\begin{center}
  \includegraphics[width=1\columnwidth]{wikihow-fig-2.pdf}
 \end{center}
  \caption{``How to Clean Suede Shoes'' article with 2 methods. Method 1 with 3 steps, method 2 with 2 steps.
  }~\label{fig:example}
\end{figure}

Clearly, multi-document summarization datasets (MDS), in which one's task is to summarize several source documents, are even harder to obtain.

To tackle this challenge, several recent works have created large-scale datasets for MDS by considering existing texts to be summaries of several other sources, as described in section ~\ref{related}.
Inspired by these works, we created a dataset based on how-to articles.
How-to articles aim at providing information about {\it how to do something}, and can cover a variety of topics (e.g. education, medicine, or cleaning).
Such articles range from a simple, ordered list of instructions to reach a goal, to less linear texts, outlining different ways to realize something, with arguments, conditions, etc.

wikiHow.com is a very popular website\footnote{According to the site itself, Comscore ranks wikiHow in the top 150 most visited publishers in the world.} consisting of how-to articles written by a community of authors on a wide variety of topics. The articles follow a well-defined structure that presents readers with instructions organized into {\it methods}
and {\it steps}.

An article contains several methods, each comprised of a title and several steps, where a step contains a title and
a text.
In addition, each article cites a set of sources (i.e., references), which are used by the article authors to synthesize the wikiHow article.
Figure~\ref{fig:example} shows a ``How to Clean Suede Shoes''\footnote{\url{https://www.wikihow.com/Clean-Suede-Shoes}} article, with methods, and steps. Method 1 is titled ``Cleaning Dirt and Scuff Marks'' in which the first step is titled ``Get a suede brush and make sure your shoes are dry''.

We leverage wikiHow to create a query-focused MDS (qMDS) dataset, named \textsc{HowSumm}, by defining the references of a wikiHow article as the set of document sources to summarize, the titles as queries, and corresponding parts of the article content as target summaries. Specifically, we extract two types of target summaries from each article -- summaries originating from steps (\textsc{HowSumm-Step}), which are typically short, and those originating from methods (\textsc{HowSumm-Method}), which are longer. Consider the example in Figure~\ref{fig:example}. The text of step 2 of method 1 (in blue) is considered as the target-summary of the source documents when using as the query the title of the step and potentially also the titles of method 1 and the wikiHow article (the three titles in red boxes). Note that \textsc{HowSumm-Step} includes five such target summaries from this specific article, corresponding to the 5 (3+2) steps. Similarly, we define the concatenation of the text of all 3 steps of method 1 as a target-summary of the source documents when using as the query the title of the method and potentially also the titles of all steps under method 1, and the wikiHow article title. \textsc{HowSumm-Method} includes two such target summaries from this specific article, corresponding to the two methods.

The resulting dataset is suitable for a use-case that is significantly different than the ones considered in other summarization datasets ;
distilling how-to instructions from the sources differs from extracting the most salient relevant information as done in News summarization, generating wikipedia paragraphs or scientific abstracts. Such a use-case, we believe, is applicable in educational and industrial applications. For example, in the technical support domain, a major task is composing troubleshooting documents that aim at solving some problem. This requires extracting the relevant problem resolution alternatives from potential sources (including, webpages, knowledge bases, white papers, etc.), and creating a coherent, actionable, and easy to follow (e.g., by using lists) instructions.

It should be noted that two large-scale summarization datasets based on wikiHow articles already exist~\cite{DBLP:journals/corr/abs-1810-09305}. One uses step title as the summary of the step's text, the other treats a concatenation of all titles in the article as summary of all other text in the article.
These datasets target the task of generic summarization using only article's text, while we target query-focused summarization using the article sources. Thus, \textsc{HowSumm} dataset is the first qMDS dataset based on wikiHow.

Our contributions are as follows: (1) We propose a new, large qMDS dataset, containing 11,121 long summaries, and 84,348 short summaries (available at \url{https://ibm.biz/BdfhzH}) based on the wikiHow website. This dataset focuses on new use-case of generating instructions from sources.
(2) We analyze the particularities of \textsc{HowSumm}'s target summaries and sources as opposed to some other MDS datasets. (3) We evaluate abstractive and extractive baselines on this new dataset.
 (4) Additionally, we propose a new methodology to ensure dataset quality leveraging previously existing human-crafted datasets.

\section{Output Examples}\label{examples}
We show in figure ~\ref{figure:summary} some examples of different summarizers outputs, generated for some instances of \textsc{HowSumm-step}.

\begingroup
\renewcommand{\arraystretch}{0.61}
\begin{figure}[h]
\centering
\resizebox{0.43\textwidth}{!}{%
\begin{tabular}{p{0.7cm}p{6.7cm}}
\multicolumn{2}{c}{{\textit{How to Choose a Medical Alert System for Seniors}}} \\
\hline
\hline
\tiny{\textbf{Method}} & \tiny{\textbf{``Deciding What Level of Monitoring Your Senior Needs''}} \\
\tiny{\textbf{Step}} & \tiny{\textbf{``Consider automatic detection features''}} \\
\hline
\tiny{\textit{Target summary}}& \tiny{Many systems offer a simple push button that allows seniors to call for help whenever they need it. There are also newer products on the market, however, that offer a higher level of protection.  Many companies now offer units with automatic fall detection capabilities, which means your senior will get emergency help even if she is unconscious or otherwise unable to press the emergency button.  Some units are also capable of automatically calling for emergency help if they detect fire or carbon monoxide.}\\
\hline
\hline
\multicolumn{2}{l}{\tiny{\textbf{Summarizers output}}} \\
\hline
\tiny{\textit{HierSumm-BM25}} & \tiny{Look for automatic features like push-button and automatic fall, emergency call center assistance, and medical monitoring.}\\
\hline
\tiny{\textit{HierSumm-Oracle}} & \tiny{Medical alert systems allow senior citizens to live on their own even, since they always have access to help whenever they need it.  For seniors who worry that they may need assistance if they fall down or meet an unexpected emergency, it provides an easy way to get access to help.}\\
\hline
\tiny{\textit{CES}} & \tiny{Some new technologies are addressing the issue by detecting falls automatically.Compared to Monitored Systems If budget allows, it is worthwhile to consider monitored medical alert systems . The system should have the features the senior needs. For an extra monthly fee, customers can get an add-on fall-detection device that will automatically call for help when needed, as long as you have a landline. I would recommend choosing a system that has the ability to upgrade features in the future and that does not have a high cancellation policy.}\\
\hline
\tiny{\textit{LexRank}} & \tiny{Some new technologies are addressing the issue by detecting falls automatically. To appeal to younger seniors and people with active lifestyles, several companies now offer more advanced systems featuring varying combinations of push-button and automatic fall detection, emergency call center assistance, and medical monitoring.A similar product is a pendant-style help button that can automatically place a call for help if it detects a fall. The system should have the features the senior needs.}\\
\hline
\\
\\
\multicolumn{2}{c}{{\textit{How to Adopt As a Single Woman}}} \\
\hline
\hline
\tiny{\textbf{Method}} & \tiny{\textbf{``Submitting Your Application''}} \\
\tiny{\textbf{Step}} & \tiny{\textbf{``Meet with your caseworker.''}} \\
\hline
\tiny{\textit{Target summary}}& \tiny{Once your application is accepted by the agency, you'll be assigned a caseworker who will be your point person throughout the adoption process. They will describe to you the next steps of the process. You need to maintain a good relationship with your caseworker. They are on your side, and want to help you adopt a child. They can answer any questions you have, as well as give you tips and advice.}\\
\hline
\hline
\multicolumn{2}{l}{\tiny{\textbf{Summarizers output}}} \\
\hline
\tiny{\textit{HierSumm-BM25}} & \tiny{Once you have completed your application, you will need to meet with a caseworker who will help you through the adoption process.  In some caseworkers, family workers who work with the children in care will work with the children in care.  you will also need to take time during the application process to get to know them as they will be your guide throughout your adoption journey.}\\
\hline
\tiny{\textit{HierSumm-Oracle}} & \tiny{Once you have completed your application, you will need to meet with your caseworker who will help you through the rest of the adoption process.  In child welfare, generally there are two types of caseworkers  :  family workers who work with families such as yours and child workers who work with children.  It is important to develop and maintain a good relationship with your caseworker, so take time during the application process to get to know them as they will be.}\\
\hline
\tiny{\textit{CES}} & \tiny{Complete Your Application to Adopt This is where the official paperwork begins and where you will meet the caseworker who will help you through the rest of the adoption process.It helps your caseworker understand your family better and assists them with writing your home study.3. Single parent adoptions are also one of the groups that adopts the most special needs children who need families. In this section you will find resources on single-parent adoption from the Child Welfare Information Gateway.}\\
\hline
\tiny{\textit{LexRank}} & \tiny{Complete Your Application to Adopt This is where the official paperwork begins and where you will meet the caseworker who will help you through the rest of the adoption process.It helps your caseworker understand your family better and assists them with writing your home study. It's important to develop and maintain a good relationship with your caseworker, so take time during the application process to get to know them as they will be your guide throughout your adoption journey.}\\
\hline

\end{tabular}
}
\caption{Ground-truth with corresponding automated summaries.}
\label{figure:summary}
\end{figure}
\endgroup
\section{Large MDS Datasets}
\label{related}
In recent years, several large-scale datasets for the task of MDS emerged
. Multi-News~\cite{fabbri-2019-multiNews} and WCEP~\cite{wcep-2020} utilize professionally edited summaries of news events. As for other domains such human summaries are hard to obtain, an approach to consider existing texts as summaries (even if they were not written with that intention in mind) was used. WikiSum~\cite{Liu-2018-wikipedia} and auto-\textit{h}MDS~\cite{zopf-2018-auto-hmds} use lead paragraphs of Wikipedia articles as summaries of their cited reference sources.
 Multi-XScience~\cite{Lu2020MultiXScienceAL} treats Related Work sections in scientific papers as summaries of cited articles' abstracts. For query focused setting, in the \textsc{AQuaMuSe} system~\cite{kulkarni2020aquamuse}, the authors propose a general method for building qMDS datasets based on question answering datasets where the answer serves as summary of relevant documents extracted from a corpus. They establish this approach by
extracting answers that are Wikipedia paragraphs from Google Natural Questions~\cite{47761} and documents from Common Crawl~\cite{raffel2020exploring} corpus.

In some cases, such as Multi-XScience and Multi-news, the documents to summarize contain only cited sources. In other cases, those are extended by search results to increase coverage.

An evident challenge when creating summarization datasets in such a way, is ensuring that the information in the suggested summary indeed exists in the suggested source documents. To this end, several methods were suggested: from using only top search results on a certain topic (WikiSum), to training a relevance classifier (WCEP), ending with using text from ground truth summary in the search for sources(\textsc{AQuaMuse}, auto-\textit{h}mds).

redpajama_set_name: RedPajamaArXiv


Each of my botanical pieces begins with a walk in the mountains and woods of Central Virginia where I gather wild plant specimens. All of my pieces are wheel-thrown using a buff-colored stoneware clay body. I press plant specimens into the wet clay, and then pull the specimen off, leaving an accurate fossil impression which gives me a template for glazing. I use a churchkey to make the repetitive point pattern at the rims and bases of some of the pieces, pressing one point at a time, free hand.
Knobs, handles and feet are hand pulled from stoneware. People often mistake my handles for wrought iron, but they are clay.
I use a combination of my own glaze recipes and commercial glazes. The glazes we mix ourselves include a green copper ash, an amber celadon, a blue celadon and a temmoku black. We use a variety of techniques to glaze each piece, including waxing, dipping, wiping and brushing. We sponge a red iron oxide wash over the point pattern and any unglazed areas.
I fire my pieces in one of two electric kilns to cone 05 for bisque and cone 7 for glazing. Bisque firing takes about 12 hours and glaze firing takes about 8-9 hours. The pieces sit in the kilns to cool for two days after the firing is completed. This helps my green glaze develop a fine pattern of crackling, which resembles old Chinese celadons.
The whole process takes three to six weeks. I lose a number of pieces to cracking, warping, or breakage at various stages of making, and to glaze faults in the firing. The pieces that meet my standards after all the firings are completed are those that I present to you in my gallery, at shows, and on this web site.


text:
「Wellknown」タグアーカイブ
Well-known influencer under fire for peddling conspiracy Brian Laundrie still alive
'This is in incredibly poor taste,' say social media users on influencer's post
Social media users are criticising Internet personality Tana Mongeau for spreading conspiracy theories about Brian Laundrie's body being misidentified.
Twenty-two-year-old Miss Mongeau has a reasonable level of influence online, with 7 million followers on TikTok and around 5.5 million subscribers on YouTube. She initially posted on Friday on Twitter, "I know Brian Laundrie pulled a Joe Goldberg", referencing the fictional psychopath protagonist in the hit Netflix tv show You who fakes his own death.
The initial post did not resonate well online, with other users calling her out for spreading false rumours. "Quit using someone's crime/murder as a joke to get some likes on social media," one person responded. "This is in incredibly poor taste," said another.
Despite the backlash, Miss Mongeau doubled down on her theory in a TikTok video, which has now been viewed around 3.5 million times.
"I'm not joking. I genuinely think something was placed there so that people would think the remains were him," she said on Tuesday.
Autopsy reports confirmed that Laundrie's dental records matched those of the remains found on 20 October at the Carlton Reserve in Florida. Laundrie was deemed a person of interest in the case of Gabby Petito, whose body was found on 19 September after she went missing in late August. Petito was killed by manual strangulation, her death was ruled a homicide.
Miss Mongeau stated on her video that "news sources confirmed" the body found was not Laundrie's, "I guarantee you he's in like Paris, France right now living his best life getting a croissant," she continued.
@tanamongeaulol
i said what i said
♬ original sound – Tana Mongeau
"Why were his remains found 24 hours after his parents started searching?" Miss Mongeau asks in the clip. "Because he is not dead."
Her rumours are backed up by more rumours: "It is rumoured that the funeral, which two people would have attended, is going to be called off," she said.
Fox News presenter Nancy Grace said on Sunday on a news segment that the circumstances of Laundrie's death were "all very vague". Laundrie's initial autopsy report is inconclusive on his cause of death.
No DNA analysis has been conducted on the remains yet, but police have refuted "false reports" that the skeletal remains found in Florida did not match his DNA.
"The identity of the remains found at the Carlton Reserve on 20 October was confirmed by comparison to known dental records of Brian Laundrie," said a North Port police department statement, released on Tuesday.
"Samples of the remains will be completed once the examination of the remains by the medical examiner's office is complete."
カテゴリー: ruf | タグ: alive, Brian, conspiracy, Influencer, Laundrie, peddling, still, under, Wellknown | 投稿日: 10/29/2021 | 投稿者: ruf


In Zabaykalsky Krai an assault on guard of a military echelon which followed on large-scale exercise "East-2018" is committed. An incident has occurred at the railway station the Petrovsky Plant on Sunday night, on August 27. The press service of the Eastern Military District has reported about it. Guard I have used weapon against group of the unknown who have broken post border. In air criminals didn't react to preventions of the sentry and a shot. One of malefactors is killed.


text:
-25°C to 120°C
Heating / cooling Rates:
0.01 °C to 20 °C/min
Working distance:
Product description Linkam Peltier Stages
The Linkam Optical Peltier stages, for thermoelectrically cooled stages on microscopes.
Linkam's PE100 and PE120 Peltier stages are easy-to-use thermoelectrically cooled hardware stages. The stages are ideally suited for non-complex – fixed temperature controls for multiple applications. The PE100 & PE120 Peltier stages do not require liquid nitrogen to cool measurements and samples below ambient temperatures.
All systems are equipped with a stage (model PE100 or PE120), a T96-P LinkPad controller which is equipped with a "touch screen" to quickly and accurately program different temperatures and an ECP water circulator. The stages are compatible with Linkam's optional LINK software, adding additional features such as data logging, graphing, data export options and imaging and image analysis modules.
Features of the PE120 Peltier stage
Wide temperature range
-25°C (depending on the water temperature) to 120°C with a stability of +/- 0.1°C.
Lens-object working distance
The "open-face stage" design meets many requirements and a working distance of up to 0.1 mm.
-10°C (depending on water temperature) to 99°C with a stability of +/- 0.1°C.
The PE100 is designed to fit into the stage of an "inverted" microscope. Linkam's Peltier stages are used in biology labs worldwide for cell research. For example sperm motility and viability of, among other things, blood and cell preservation at low temperatures. The flat design of the stage provides full access to the sample and facilitates the use of Petri dishes, microscope slides and perforated sample plates. Manufacturer-specific versions of the PE100 series are available for Leica, Nikon, Olympus and Zeiss microscopes.
Temperatuur range
werkafstand


text:
Shocking Revelations From Hunter Biden's Laptop: Drugs, Prostitutes, Federal Crimes
Posted by LTP Support | Apr 12, 2021 | More News, Political |
By JOSH BOSWELL FOR DAILYMAIL.COM
Hunter's laptop is brimming with evidence of apparent criminal activity by him and his associates including drug trafficking and prostitution. This photo of him grabbing a woman's hair was recovered from his laptop
EXCLUSIVE: What WASN'T in Hunter Biden's book: How he got unauthorized Secret Service protection, begged Joe to run for WH to salvage his own reputation and made porn films with prostitutes. Forensic experts prove laptop IS President's son's
· DailyMail.com can reveal those secrets after contents of his abandoned laptop – including a cache of 103,000 text messages, 154,000 emails, more than 2,000 photos – were verified by top forensics experts
· Hunter left his MacBook Pro laptop at a Wilmington, Delaware computer repair shop in April 2019 and never returned for it
· In texts from 2019, Hunter begged his father to run for president to salvage Hunter's own reputation
· Hunter repeatedly dodged police action against him, despite constantly dealing with drug dealers and prostitutes and having multiple run-ins with the law
· The president's son was guarded by a Secret Service agent while on a 2018 drug and prostitute binge in Hollywood, despite not being entitled to protection at the time
· Hunter appeared to be obsessed with making porn films with prostitutes, videos and photos on his laptop show
· Texts also show Joe Biden was afraid his conversations with Hunter were being hacked even as they discussed his White House bid
Hunter Biden's 'tell-all' autobiography promised the unvarnished story of his struggles with drugs, family turmoil and his controversial love life.
But there were many shocking details of his debauched life that the president's son chose not to share with readers, including details of his fragile relationship with his father.
Now DailyMail.com can reveal the secrets Hunter chose not to divulge, exposed in the contents of his abandoned laptop and verified by top forensics experts.
The bombshell cache of 103,000 text messages, 154,000 emails, more than 2,000 photos and dozens of videos are packed with revelations conveniently missing from the memoir, including:
How he begged his father to run for president in 2019 to salvage Hunter's own reputation
How he repeatedly dodged police action against him, despite constantly dealing with drug pushers and prostitutes and having multiple run-ins with law enforcement
Hunter was guarded by a Secret Service agent while on a 2018 drug and prostitute binge in Hollywood, despite not being entitled to protection at the time and amid denials from the federal agency
Joe Biden was afraid his text conversations with Hunter were being hacked even as they discussed his White House bid
How Hunter's laptop is brimming with evidence of apparent criminal activity by Hunter and his associates including drug trafficking and prostitution
HERE ARE THE DISTURBING DETAILS
Hunter Biden released his 'tell-all' memoir Beautiful Things on Tuesday, but left out shocking details. DailyMail.com can reveal those secrets after contents of his abandoned laptop – including a cache of 103,000 text messages, 154,000 emails, more than 2,000 photos – were verified by top forensics experts
Hunter left his MacBook Pro laptop at a Wilmington, Delaware computer repair shop in April 2019 and never returned for it. The report said the data on the drive 'appear to be authentic', and that after an extensive search of its contents for any tell-tale signs of tampering, Maryman and Greenfield found 'no evidence' of fabrication – by Russians or anyone else.
The texts reveal how Joe was sucked into his son's torrid love life, became a punching bag for Hunter's drug-fueled rants, and paid his grandchildren's bills when Hunter had drained his bank accounts with prostitutes and crack cocaine
In texts from February 2019, Hunter begged his father Joe to run for president to salvage Hunter's own reputation. 'H is wrong,' Joe writes, referring to Hallie Biden.
On Hunter's laptop he had his own profile on porn site Pornhub where there was content of him in bed with two woman with a small dog nearby. Hunter photographed and filmed himself, often with two prostitutes at a time, in explicit videos
The president's son left his 2017 MacBook Pro laptop at a Wilmington, Delaware computer repair shop in April 2019 and never returned for it.
Emails leaked from the computer sparked a scandal before the 2020 presidential election in October, but were dismissed as Russian disinformation by the Bidens and other senior Democrats, social media giants Twitter and Facebook, and several major media organizations.
After obtaining a copy of the hard drive, DailyMail.com commissioned top cyber forensics experts Maryman & Associates to analyze its data and determine whether the laptop's contents were real.
The firm's founder, Brad Maryman, is a 29-year FBI veteran Supervisory Special Agent who served as an Information Security Officer and founded its first computer forensics lab.
Maryman and his business partner Dr. Joseph Greenfield used the same forensic tools to pick apart the drive as federal and state law enforcement use in criminal investigations, and prepared a report for DailyMail.com.
The report said the data on the drive 'appear to be authentic', and that after an extensive search of its contents for any tell-tale signs of tampering, Maryman and Greenfield found 'no evidence' of fabrication – by Russians or anyone else.
'The data on the system before April 2019 appears to be related to Mr. Biden, with timestamps appearing to be between 2016 and 2019,' the Maryman and Associates report said.
'The operating system timestamps appear to be authentic, and no evidence was found to suggest that the timestamps or data were altered or manufactured. No indications were found that would indicate the data was manufactured.'
In a presidential debate in October, Joe Biden referred to the leaks of Hunter's emails as 'a bunch of garbage.'
But in an interview with CBS on Sunday to promote his new book, Beautiful Things, Hunter said the laptop 'could be' his.
'There could be a laptop out there that was stolen from me. It could be that I was hacked. It could be that it was the — that it was Russian intelligence. It could be that it was stolen from me or that there was a laptop stolen from me,' he said.
When Joe texted him a typically doting message 'Good morning my beautiful son. I miss you and love you. Dad' on February 24 that year, Hunter raged at his father for 'having made clear to the world that the only reason for not [running for president is your] family problems im glad to be the f***ing bullseye you painted on my back'
A shocking photo obtained from Hunter's laptop shows his badly damaged and worn down teeth – also known as 'meth mouth' – as he sits in a dentist chair
Among the 103,199 text messages on the hard drive dating from 2016 to March 2019 are fraught conversations between President Joe Biden and his son.
The texts reveal how Joe was sucked into his son's torrid love life, became a punching bag for Hunter's drug-fueled rants, and paid his grandchildren's bills when Hunter had drained his bank accounts from his spending on prostitutes and crack cocaine.
When the former Delaware senator was elected Vice President in 2008, it put an end to Hunter's lucrative career as a lobbyist to avoid the appearance of conflicts of interest.
Hunter then embarked on a series of controversial business ventures and fell into a cycle of crack and prostitute binges, scandal and rehab.
By 2019 his texts appeared to be seething with resentment.
When Joe texted him a typically doting message 'Good morning my beautiful son. I miss you and love you. Dad' on February 24 that year, Hunter raged at his father for 'having made clear to the world that the only reason for not [running for president is your] family problems im glad to be the f***ing bullseye you painted on my back.'
Hunter complained that his father's advice to not defend himself in the media over stories about his expensive divorce from his ex-wife Kathleen Biden or his affair with his brother's widow Hallie had backfired, and added sarcastically: 'Oh …good morning …from f***ing rehab.'
'Your team just made me the uncontrollable troubled tax cheat philanderer sex and drug addict that you tried so hard to fix but couldn't yt. They just totally wrote my life away,' he wrote.
'If you dont run ill never have a chance at redemption.'
Joe replied, promising his son he would run for President but adding that he was worried his texts were 'a target' for hacking.
'I'll run but I need you,' he wrote. 'H[allie] is wrong. Only focus is recovery. Nothing else… When you can and feel like it call. Positive my text etc a target. Love.'
He declared his candidacy two months later.
After another rant from Hunter in September 2018 Joe wrote: 'Be careful what you text. Likely I'm being hacked.'
The future president had good reason to worry, as his phone was filling up with incriminating and embarrassing texts as his son's relationship with Hallie disintegrated, according to Hunter's messages to him.
Hunter Biden describes falling in love with his grieving sister-in-law Hallie Biden (pictured) after his brother Beau's death, in his new memoir
When Hunter's controversial affair with Beau's widow was publicly revealed in March 2017, Joe reluctantly released a statement giving the couple his blessing.
But a year later he was already being drawn into the couple's meltdown by his erratic, addict son.
In July 2018 Hunter sent a recording to his father of himself talking on the phone to Hallie.
Joe responded: 'Got your text. Can't say I'm surprised. Going to bed I'll call you tomorrow morning. Love you. Be strong. Love Dad.'
The president's continued forgiveness and offers of money via text to his son after his crack binges – are at odds with his long history of tough stances against drug users and suppliers.
Joe criticized George Bush Sr's 1980s War on Drugs for being 'not tough enough' and wrote punitive laws with harsher sentences for drug crimes when he was head of the Senate Judiciary Committee in the late 1980s and early 1990s.
His son's drug-fueled calamities have resulted in multiple run-ins with law enforcement that would usually result in criminal charges or investigation, but Hunter has so far avoided the dock.
His laptop, which was handed over to the FBI in 2019, is brimming with evidence of apparent criminal activity by Hunter and his associates including drug trafficking and prostitution.
Photos also show Hunter with cracked and badly damaged teeth – known as 'meth mouth' – as he sits in a dentist chair, and another photo showing him visibly balding.
Texts show Joe Biden was afraid his conversations with Hunter were being hacked even as they discussed his White House bid
Hunter appeared to be obsessed with making and starring in porn films with prostitutes, videos and photos on his laptop show.
The hard drive contains hundreds of pictures of naked women and naked selfies of Hunter, as well as dozens of videos.
Hunter photographed and filmed himself, often with two prostitutes at a time, in explicit videos that he then posted on adult website Pornhub under the username 'RHEast'.
Hunter filmed himself with the women from his laptop webcam, sometimes shooting at different angles using an iPad and cell phone.
The sex tapes and shots with hookers are mixed in with family photos, business documents and selfies from his then-lover and brother's widow Hallie in his laptop's photos folder.
Texts show he would often order drugs to hotel rooms and take them with the women before or after sex – and photos include bottles of 100mg Viagra pills, the highest dose prescribed by doctors.
His Pornhub account had 66 award badges, including a badge for reaching 50 subscribers, watching 500 videos, and watching porn in high definition.
Hunter would sometimes leave on the webcam after filming his sex scenes, and captured himself hunched over his computer, intently watching back through the footage.
In one video on the laptop, Hunter recorded his screen and can be heard speaking to a woman about his favorite porn stars as he browsed an adult website with her.
Texts from February 20, 2019 appear to show Hunter coordinating with a woman called Dianna Pagano to transport other women in Ubers to his hotel room in New Haven, Connecticut.
A month later Pagano texted Hunter offering to 'hang' with him for '100 or 200 for the night', as well as appearing to offer him drugs, saying 'My guy is giving me 2500qorrh of work cuz he is going on vacation. I just gotta give him 200$; and 'I got some good work if u need anything hun its fire'.
Pagano was charged in November that year with violating her probation by allegedly running a drug ring out of her home in East Haven.
Police told the New Haven Register that items 'believed to be the fruits of a narcotics and prostitution operation, [were] found within the residence.'
Pagano was not charged with a prostitution-related offence.
Hunter may have narrowly avoided a tangle with law enforcement, as six of Pagano's alleged customers were also arrested in the police raid.
Hunter had a second close shave with the law in March 2019 according to his texts, when police allegedly showed up at a Naugatuck, Connecticut, motel where Hunter was taking drugs and entertaining hookers.
When the president's son left his Comfort Inn room to get cash from an ATM, one of the panicked prostitutes, Crystal, texted him 'U called the cops???'
Returning to his room a confused Hunter texted their dealer and pimp 'Where is she? She said the cops came to the room????? WTF man? I went to bank. Call me back.'
Naugatuck Police Department told DailyMail.com they had no record of officers attending the Comfort Inn on March 7.
Hunter had a close shave with the law in March 2019 according to his texts, when police allegedly showed up at a Naugatuck, Connecticut, motel where Hunter was taking drugs and entertaining hookers. When the president's son left his Comfort Inn room to get cash from an ATM, one of the panicked prostitutes, Crystal, texted him 'U called the cops???'
Naugatuck Police Department told DailyMail.com they had no record of officers attending the Comfort Inn on March 7. Returning to his room a confused Hunter texted their dealer and pimp 'Where is she? She said the cops came to the room????? WTF man? I went to bank. Call me back'
Hunter's laptop, which was handed over to the FBI in 2019, is brimming with evidence of apparent criminal activity by Hunter and his associates including drug trafficking and prostitution.
Hunter did come under investigation after he dropped off a rental car in Prescott, Arizona, leaving behind a crack pipe and white powder, his drivers license and his late brother Beau's attorney general badge.
Hunter allegedly used a fake name to call the Hertz rental the next day to tell them he left the keys in the car's gas tank compartment.
The police report said they called in the FBI and Hunter was 'located by Secret Service' and found to be 'well.'
Prescott Police Department said they couldn't find fingerprints on the pipe. It is unclear whether the department tried to extract DNA from the pipe.
Despite being dubbed the state's 'foremost pot prohibitionist', County Attorney Sheila Polk declined to prosecute.
Hunter was investigated but again not prosecuted in 2018 after Hallie left his .38 caliber handgun in a Wilmington, Delaware grocery store trash can.
In texts to his therapist, Hunter claimed that when Delaware cops found the gun 'the FBI and the Secret Service' got involved, and that Hallie told officers she took the weapon because she was 'afraid for the kids' and 'because she was scared I would harm myself due to my drug and alcohol problem'.
A copy of a police report obtained by news site Politico confirmed the October 23, 2018 incident.
The site reported that Hunter may have lied on his background check to obtain the gun, which would be a felony if true.
In texts obtained by DailyMail.com, Hunter messaged his therapist to tell him about Hallie dropping his gun in a grocery store trash can. 'She stole the gun out of my truck lock box and threw in a garbage can full to the top at Jansens. Then told me it was my problem to deal with,' Hunter wrote.
One question on the background check document is: 'Are you an unlawful user of, or addicted to, marijuana or any depressant, stimulant, narcotic drug, or any other controlled substance?'
Hunter reportedly checked 'No' on the form, despite photos and texts from his laptop showing heavy drug use at the time, including crack cocaine.
Prosecutions for lying on such forms are very rare, and police did not pursue Hunter over the apparent infraction.
According to Hunter's texts to his therapist about the incident, the Secret Service were also involved in the investigation.
The Department of Homeland Security agency claimed they had no record of agents investigating the incident, and Joe Biden, who was not under their protection at the time, said he had no knowledge of their involvement.
But Hunter's texted claims that the agency was involved are echoed by two sources who spoke to Politico.
One source said Secret Service agents asked the Wilmington, Delaware gun store owner where Hunter bought the .38 for the sale paperwork.
The source, who the site reported had firsthand knowledge of the episode, said that the store owner, Ron Palmieri, refused to hand over the papers as he feared the agents wanted to hide Hunter's ownership of the gun.
The source said Palmieri instead later turned over the documents to the Bureau of Alcohol, Tobacco, Firearms and Explosives.
A second source who was reportedly briefed by the Secret Service confirmed the story to the site.
Texts from earlier that year show Hunter was being guarded by a Secret Service agent while he went on a week-long drug and prostitute binge in a Hollywood hotel.
The texts raise questions for the federal agency, which has claimed that no Bidens received protection in 2018.
The agent added that he was working on 'Celtic's account', an apparent code name for a senior politician under protection, and told the president's son 'DC is calling me every 10.' The Bidens lost their protection by federal agents after Joe ended his term as Vice President in January 2017, and did not officially regain a detail until congressional officials approved his application in Spring last year.
In the messages, a Los Angeles-based Secret Service officer warned Hunter he would 'have to assume you are in danger' and commandeer keys to the room if he didn't come out.
The agent added that he was working on 'Celtic's account', an apparent code name for a senior politician under protection, and told the president's son 'DC is calling me every 10.'
Press releases by a private security firm published a week after the text exchange with Hunter, announced the agent left the Secret Service after a 25-year career and joined the firm as CEO.
The Bidens lost their protection by federal agents after Joe ended his term as Vice President in January 2017, and did not officially regain a detail until congressional officials approved his application in Spring last year.
A bill from The Jeremy Hotel in West Hollywood obtained by DailyMail.com shows that Hunter stayed in a third-floor suite for seven days from May 19 2018, holed up ordering room service each night, drinking at the hotel bar and racking up a $5,195 bill – including a $400 fine for smoking in his room.
Texts on the laptop show that during his lavish stay Hunter sought refills for prescriptions of Viagra generic equivalent sildenafil, an antidepressant Lexapro, and anxiety medication Clonazepam, a similar drug to Xanax.
Emails from May 24 show Hunter added a prostitute as a wire transfer recipient on his bank account and wired out $25,000.
DailyMail.com obtained a copy of Hunter's laptop hard drive from former Steve Bannon podcast co-host Jack Maxey.
John Paul Mac Isaac, the owner of the computer store where Hunter abandoned his laptop, gave a copy of its hard drive to Donald Trump's lawyer Rudy Giuliani in September last year.
Giuliani reportedly leaked documents and photos from the drive to the New York Post, and also gave a whole copy of the drive to Trump's former advisor Bannon and his podcast co-host Maxey.
Hunter, the Secret Service, their agent who now works for a private company and the White House did not respond to requests for comment.
AUTHENTICATING THE LAPTOP
DailyMail.com commissioned cyber forensics experts at Maryman & Associates to examine the hard drive to determine its authenticity.
The firm's founder, Brad Maryman, was a 29-year veteran of the FBI, served as a Chief Information Security Officer and founded the bureau's cyber forensics unit. His partner, Dr. Joseph Greenfield, is an associate professor at the University of Southern California and helped write their degree program in intelligence and cyber operations.
After an extensive analysis of the hard drive, Greenfield and Maryman produced a report for DailyMail.com detailing their findings.
Using the same forensic tools as federal and state law enforcement in criminal investigations, they found a total of 103,000 text messages, 154,000 emails and more than 2,000 photos.
– They found emails for multiple accounts on the laptop dating back to 2009, and other data which 'appears to be related to Mr. Biden' between 2016 and 2019.
– The report's findings were consistent with the known timeline for the hard drive. A Wilmington, Delaware computer store work order with Hunter's signature shows he left his 2017 MacBook Pro laptop there on April 12, 2019.
– The Maryman & Associates report said the original 'Macintosh HD' drive was created on March 28, 2018
– Hunter's iCloud email address was added to the laptop's system on October 21 2018, as well as his work email at his firm Rosemont Seneca on February 2 2019.
– The same day, a Gmail address he used to log onto sex cam sites, and another personal Gmail address belonging to Hunter, were also added.
– Beau Biden's old Gmail account was added on February 7 2019.
– Emails addressed to Hunter's various email addresses dating from December 2009 to December 2020 were found on the system.
– An iPad with the name 'Hunter's iPad' and three email addresses associated with the Biden family was backed up on the laptop and on iCloud in January 2019 and again a month later.
– Greenfield found 818 call logs in this iPad backup with timestamps from June 2016 to February 2019.
– There were 8,942 entries in the iPad's contacts book, created between April 2016 and January 2019.
– In February 2019 an iPhone XS was also synced with the laptop. Its serial number was consistent with the timestamps of the data on the phone.
In conclusion, 'The operating system timestamps appear to be authentic, and no evidence was found to suggest that the timestamps or data were altered or manufactured,' the report said. 'No indications were found that would suggest the data was manufactured.'
CLICK HERE TO READ THIS ENTIRE SHOCKING ARTICLE THAT HAS LURID PHOTOS AND COPIES OF EXACT TEXT MESSAGES.
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text:
ALEJANDRO MENDOZA,
DCS SANITATION
ZURICH U.S. INSURANCE COMPANY,
1. The Workers Compensation Act specifically adopts the Kansas Act for Judicial Review and Civil Enforcement of Agency Actions, K.S.A. 77-601 et seq., for workers compensations cases. An appellate court's review of the Workers Compensation Board's decision shall be upon questions of law. The interpretation of K.S.A. 2006 Supp. 44-508(f)–containing the coming and going rule and its exceptions–is a question of law subject to de novo review.
2. The Kansas Workers Compensation Act, specifically K.S.A. 2006 Supp. 44-501(a), provides compensation for injuries to an employee which arise out of and in the course of employment. Whether an accident arises out of and in the course of employment depends on the facts peculiar to each case. Generally, an injury suffered when an employee travels to and from work does not arise out of and in the course of employment.
3. Our appellate courts have historically recognized a major exception to the going and coming rule. Where the going and coming of an employee is actually contemplated by the employment itself, an injury occurring during the undertaking is compensable under the Workers Compensation Act. This exception is referred to as the "work-related errand" or "special purpose trip" exception because injuries that are incurred while going and coming from places where work-related tasks occur are compensable where the traveling is required in order to complete some special work-related errand or special purpose trip in the scope of the employment.
4. The "work-related errand" exception extends to the normal risks involved in completing the required task or travel; the entire task or trip must be viewed as unitary or indivisible, meaning an injury during any aspect thereof is compensable.
5. The business of employment in this state necessarily includes the payment of wages to employees. Where the employer has directed employees to pick up paychecks at a location separate and apart from the workplace, the employee's trip to do so is a business mission or work-related errand that is necessary to the employment. An employee's trip to the employer's off-worksite location for this purpose falls squarely within the work-related errand exception to the going and coming rule, and an injury sustained during such trip is fully compensable under the Workers Compensation Act.
Appeal from the Workers Compensation Board. Opinion filed March 9, 2007. Reversed and remanded with directions.
Stanley R. Ausemus, of Stanley R. Ausemus, Chartered, of Emporia, for appellant.
Richard L. Friedeman, of Watkins Calcara, Chtd., of Great Bend, for appellee.
Before RULON, C.J., GREENE and McANANY, JJ.
GREENE, J.: Alejandro Mendoza appeals the decision of the Workers Compensation Board (Board) that found his injury did not arise out of and in the course of his employment with DCS Sanitation when he fell outside the offices of his employer where he had been directed to pick up his paycheck for work at another location. He argues the Board should have applied the recognized exception to the going and coming rule for a task that was necessary to his employment and that furthered the interests of his employer, so that his injury in his travels in connection with that task should have been compensable. We agree with Mendoza, reverse the Board's decision, and remand with directions.
Alejandro Mendoza worked for DCS Sanitation (DCS) at the National Beef Packing Company in Liberal, Kansas. Mendoza did not receive his paycheck at the packing plant, however, but was directed to pick it up at a separate office of DCS each Thursday or Friday. Mendoza was apparently offered no alternative means of being paid, such as direct deposit or otherwise.
On December 5, 2002, snow and ice covered the ground when Mendoza drove to the DCS office to pick up his paycheck. When he arrived at the office, he parked in the lot adjacent to the building. Yolanda Zavala, another employee of DCS, also parked in the lot and was delivering the paychecks to the DCS office. She asked Mendoza to help her carry a briefcase because she had three other bags to carry. He agreed, and upon almost reaching the DCS office, he slipped, fell, and broke his right ankle.
Ultimately, Mendoza had two operations on his ankle. Although he received checks for the first month he missed work, he has not received any other benefits or compensation from DCS. He now owes $3,705.11 for a medical bill, an outpatient bill of $176.85, and an unpaid prescription bill of $28.36. In preparation for his workers compensation claim, two doctors examined Mendoza's ankle. On September 30, 2004, Dr. Pedro A. Murati examined Mendoza. He testified that Mendoza suffered a 15% permanent partial impairment to his right ankle. On August 2, 2005, Dr. C. Reiff Brown examined Mendoza. He determined that Mendoza suffered an 11% permanent partial impairment to the ankle.
On October 28, 2003, an administrative law judge ruled Mendoza's injury arose out of and in the course of his employment and ordered DCS and its insurer, Zurich U.S. Insurance Company, to pay his medical bills until "further order or until certified as having reached maximum medical improvement." DCS and Zurich appealed this decision to the Board. On December 30, 2003, one member of the Board reversed the judge's decision and held that Mendoza's injury was not compensable.
On an appeal to the five-member Board, the initial ruling was upheld and Mendoza was denied workers compensation benefits. The Board expressly found that DCS "directed its employees to come to its business office to pick up their paychecks" and that this procedure "benefitted respondent." It denied compensability, however, because Mendoza's travel did not "comprise a business mission or errand." Two Board members dissented, stating in part that DCS directly benefitted from the practice of paycheck pick up and "this transformed claimant's travel to a business errand." Mendoza appeals.
The Workers Compensation Act specifically adopts the Kansas Act for Judicial Review and Civil Enforcement of Agency Actions, K.S.A. 77-601 et seq., for workers compensations cases. K.S.A. 2006 Supp. 44-556(a). The appellate court's review of the Board's decision "shall be upon questions of law." K.S.A. 2006 Supp. 44-556(a). The interpretation of K.S.A. 2006 Supp. 44-508(f)–containing the coming and going rule and its exceptions–is a question of law subject to de novo review. Although the Board's interpretation of the statute is persuasive, it is not binding on the courts. Foos v. Terminix, 277 Kan. 687, 692-93, 89 P.3d 546 (2004).
To the extent the Board has based its action on a determination of fact, whether implied or contained in findings of fact, we must determine whether such fact is supported by evidence that is substantial when viewed in light of the record as a whole. K.S.A. 77-621(c)(7). To the extent that the ultimate issue here is whether an accident arises out of and in the course of employment entitling Mendoza to compensation, this is a question of fact. See Titterington v. Brooke Insurance, 277 Kan. 888, 894, 89 P.3d 643 (2004).
Did the Board Err in Its Application of the Going and Coming Rule
and Its Recognized Exceptions to Deny Benefits to Mendoza?
The Kansas Workers Compensation Act provides compensation for injuries to an employee which arise "out of and in the course of employment." K.S.A. 2006 Supp. 44-501(a). Whether an accident arises out of and in the course of employment depends on the facts peculiar to each case. Newman v. Bennett, 212 Kan. 562, Syl. ¶ 3, 512 P.2d 497 (1993).
Generally, an injury suffered when an employee travels to and from work does not arise out of and in the course of employment. K.S.A. 2006 Supp. 44-508(f). "The rationale for the 'going and coming' rule is that while on the way to or from work the employee is subjected only to the same risks or hazards as those to which the general public is subjected. Thus, those risks are not causally related to the employment. [Citations omitted.]" Thompson v. Law Offices of Alan Joseph, 256 Kan. 36, 46, 883 P.2d 768 (1994).
Our appellate courts have historically recognized a major exception to the going and coming rule. Where the going and coming of an employee is "actually contemplated by the employment itself," an injury occurring during the undertaking is compensable under the Workers Compensation Act. Blair v. Shaw, 171 Kan. 524, 528, 233 P.2d 731 (1951) (entire trip by mechanics for annual certification test was integral to employment, causing deaths during trip to be compensable); see cases of likekind although factually dissimilar collected at 171 Kan. at 528.
As most often quoted, our court articulated this exception in Messenger v. Sage Drilling Co., 9 Kan. App. 2d 435, Syl. ¶ 2, 680 P.2d 556, rev. denied 235 Kan. 1042 (1984) (compensable injury when oil field employee killed in truck accident on the way home from distant drill site), as follows:
"One very basic exception to the 'going and coming' rule applies when the operation of a motor vehicle on the public roadways is an integral part of the employment or is inherent in the nature of the employment or is necessary to the employment, so that in his travels, the employee was furthering the interests of his employer."
The exception has been referred to as the "work-related errand" or "special purpose trip" exception because we have held that injuries incurred while going and coming from places where work-related tasks occur can be compensable where the traveling is required in order to complete some special work-related errand or special purpose trip in the scope of the employment. See, e.g., Ridnour v. Kenneth R. Johnson, Inc., 34 Kan. App. 2d 720, Syl. ¶ 5, 124 P.3d 87 (2005), rev. denied 281 Kan. ___ (2006) (injury to a worker driving home in order to pick up keys so he could let his coworkers into a worksite was compensable); Brobst v. Brighton Place North, 24 Kan. App. 2d 766, 774-75, 955 P.2d 1315 (1997) (injury sustained in parking lot after attending a continuing education seminar was compensable).
As emphasized by our court in both Blair and Brobst, the exception extends to the normal risks involved in completing the task or travel, and the required perspective is to view the task or trip as unitary or indivisible, meaning an injury during any aspect thereof is compensable. See Blair, 171 Kan. at 529; Brobst, 24 Kan. App. 2d at 775. As long as the employee's trip or task "is an integral or necessary part of the employment," this exception applies to assure compensability for an injury suffered during any portion of such trip or task. See Kindel v. Ferco Rental, Inc., 258 Kan. 272, 277, 899 P.2d 1058 (1995) (injury to a worker was compensable where he drove a great distance to a construction site and then spent numerous hours drinking at a strip club before returning home and being injured in route); Angleton v. Starkan, Inc., 250 Kan. 711, 828 P.2d 933 (1992) (truck driver sustained compensable injury where shot and killed during a hijacking of his truck while hauling load of cattle for employer).
DCS acknowledges the Messenger exception to the going and coming rule but argues that "it cannot be said that what [Mendoza] was doing in the instant case was an integral part or necessary to his employment." We find this a curious argument; when an employer directs an employee to pick up his or her paycheck at a location separate and apart from the place of employment, should this be characterized as unrelated or unnecessary to the employment? We think not.
In Kansas, employers are required by statute to pay wages, and the frequency of payment, the nature of payment, and the place of payment are restricted by law for the benefit of employees. See K.S.A. 44-314; see also Palmer v. Lindberg Heat Treating, 31 Kan. App. 2d 1, 2, 59 P.3d 352 (2002) (picking up paycheck was part of employment relationship of employees with employer). The "business" of employment in this state necessarily includes the payment of wages to employees. Where the employer has directed employees to pick up paychecks at a location separate and apart from the workplace, the employee's trip to do so is a business mission or work-related errand that is integral to the employment. Mendoza's trip to DCS' office for this purpose fell squarely within the recognized work-related errand exception to the going and coming rule, and his injury sustained during this trip was fully compensable as a matter of law. This holding is consistent with the Board's finding that the paycheck pick-up directive "benefitted" the employer, but the Board erred in denying Mendoza application of the recognized exception.
Mendoza urges other grounds to support the compensability of his injury, but our conclusion above renders these arguments moot.
Reversed and remanded for further proceedings not inconsistent with this opinion.
Updated: March 09, 2007; revised May 16, 2007.


VARG – SIGNS WORLDWIDE DEAL WITH NAPALM RECORDS!
Relentless and controversial: These are just some of the traits used to describe the five wolves of VARG who have just signed a worldwide deal with Napalm Records!
Since their founding in 2005 in Coburg, Germany, the band has made quite an astonishing career for themselves. They have released four full-length albums, which all had chart entries in Germany and Austria. The band has toured relentless throughout their career including several North American tours. All of which has made VARG an internationally recognized sensation.
These Germans hit the listener right where it hurts with their intense lyrics and modern Viking metal sounds. While the music is quite rich in variety, VARG has always made a conscious effort to stay true to themselves and their influences.
The wolves are roaming once again and are set to release their brand new album Das Ende aller Lügen on Napalm Records - Albumteaser can be found HERE! More info on the album will be coming soon!
10.07.2015 DE – Rieden Kreuth / G.O.N.D.