text:
Using age distributions from tributaries
Assessing the method on synthetic distributions
Applications to a detrital age dataset
Mineral concentration factors and their uncertainty
Ways in which the method could be improved
Code and data availability
ESurf | Articles | Volume 6, issue 1
Research article 29 Mar 2018
Research article | 29 Mar 2018
Extracting information on the spatial variability in erosion rate stored in detrital cooling age distributions in river sands
Erosion rate from detrital age distributions
Extracting information on the spatial variability in erosion rate stored in detrital cooling age distributions in river sands Erosion rate from detrital age distributions Jean Braun et al.
Jean Braun1,2, Lorenzo Gemignani3, and Peter van der Beek4 Jean Braun et al. Jean Braun1,2, Lorenzo Gemignani3, and Peter van der Beek4
1Helmholtz Centre Potsdam, GFZ German Research Center for Geosciences, Potsdam, Germany
2Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
3Department of Earth Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, the Netherlands
4ISTerre, Université Grenoble Alpes, CS 40700, 38058 Grenoble CEDEX 9, France
Received: 03 Jul 2017 – Discussion started: 13 Jul 2017 – Revised: 02 Mar 2018 – Accepted: 22 Mar 2018 – Published: 29 Mar 2018
One of the main purposes of detrital thermochronology is to provide constraints on the regional-scale exhumation rate and its spatial variability in actively eroding mountain ranges. Procedures that use cooling age distributions coupled with hypsometry and thermal models have been developed in order to extract quantitative estimates of erosion rate and its spatial distribution, assuming steady state between tectonic uplift and erosion. This hypothesis precludes the use of these procedures to assess the likely transient response of mountain belts to changes in tectonic or climatic forcing. Other methods are based on an a priori knowledge of the in situ distribution of ages to interpret the detrital age distributions. In this paper, we describe a simple method that, using the observed detrital mineral age distributions collected along a river, allows us to extract information about the relative distribution of erosion rates in an eroding catchment without relying on a steady-state assumption, the value of thermal parameters or an a priori knowledge of in situ age distributions. The model is based on a relatively low number of parameters describing lithological variability among the various sub-catchments and their sizes and only uses the raw ages. The method we propose is tested against synthetic age distributions to demonstrate its accuracy and the optimum conditions for it use. In order to illustrate the method, we invert age distributions collected along the main trunk of the Tsangpo–Siang–Brahmaputra river system in the eastern Himalaya. From the inversion of the cooling age distributions we predict present-day erosion rates of the catchments along the Tsangpo–Siang–Brahmaputra river system, as well as some of its tributaries. We show that detrital age distributions contain dual information about present-day erosion rate, i.e., from the predicted distribution of surface ages within each catchment and from the relative contribution of any given catchment to the river distribution. The method additionally allows comparing modern erosion rates to long-term exhumation rates. We provide a simple implementation of the method in Python code within a Jupyter Notebook that includes the data used in this paper for illustration purposes.
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How to cite.
Braun, J., Gemignani, L., and van der Beek, P.: Extracting information on the spatial variability in erosion rate stored in detrital cooling age distributions in river sands, Earth Surf. Dynam., 6, 257-270, https://doi.org/10.5194/esurf-6-257-2018, 2018.
Thermochronometric methods provide us with information pertaining to the cooling history of a rock. Various systems and minerals provide information on different parts of that cooling history, i.e., at a given temperature but more commonly within a range of temperatures. One of the main geological processes through which rocks experience cooling is exhumation towards the cold, quasi-isothermal surface (Brown, 1991). Young ages are commonly interpreted to indicate recent or rapid exhumation whereas old ages usually correspond to ancient or slow exhumation. Cooling ages can also record more discrete cooling events such as mineral crystallization during melt solidification, the nearby emplacement of hot intrusions (Gleadow and Brooks, 1979) or the rapid relaxation of isotherms at the end of an episode of rapid erosion (Braun, 2016; Kellett et al., 2013).
Besides collecting in situ data, one can also collect and date a large number of mineral grains from a sand sample collected at a given location in a river draining an actively eroding area. Such detrital thermochronology datasets provide a proxy for the distribution of surface rock ages in a given catchment (Bernet et al., 2004; Brandon, 1992). By repeating this operation at different sites along a trunk stream, one obtains redundant information that can be used to document more precisely the spatial variability in in situ thermochronological ages in a river catchment (Bernet et al., 2004; Brewer et al., 2006).
Methods have been devised to extract quantitative information from such detrital datasets concerning the erosion history of a tectonically active area as well as estimates of its spatial variability. Ruhl and Hodges (2005) convolved their detrital age datasets with the hypsometry of the catchment to test the assumption of topographic steady state in a rapidly eroding catchment of the Nepalese Himalaya. Similarly, focusing on the eastern Sierra Nevada and White Mountains of California, respectively, Stock et al. (2006) and Vermeesch (2007) combined detrital apatite (U-Th)/He age datasets with an age–elevation relationship established from in situ samples to predict the distribution of present-day erosion rates. Whipp et al. (2009) used simulations from a thermo-kinematic model to define the limits of the applicability of such a technique, while Enkelmann and Ehlers (2015) used it in a glaciated landscape. Wobus et al. (2003, 2006) collected samples from tributaries of the Burhi Gandaki and Trisuli rivers to document the strong transition in erosion rate across a major topographic transition. By limiting their sampling to tributaries, they circumvented the need to develop and use a mixing model for the interpretation of their data. Brewer et al. (2006) derived optimal values for erosion rate in neighboring catchments by comparing and mixing theoretical probability density distributions with detrital age data from the Marsyandi River in Nepal. Resentini and Malusà (2012) used a similar approach to interpret data from the Western Alps. McPhillips and Brandon (2010) used detrital cooling ages combined with in situ age measurements to infer a recent increase in relief in the Sierra Nevada, California.
All of these methods rely on a priori knowledge or hypotheses concerning the age distributions in the catchments drained by the river from which the samples have been collected. Here we explore the possibility of deriving first-order information about the spatial variability in erosion rate in the river catchment when no such knowledge exists and without making any assumption concerning the spatial distribution of ages. We propose to regard ages as passive markers (or tracers) that inform us on the proportion in which mixing takes place today, which must be directly proportional to the present-day erosion rate. For example, the most rapid present-day erosion rates should be predicted where the age distributions change the most rapidly along the river, everything else being accounted for, such as the relative size of neighboring sub-catchments or the potential change in lithology between them. This knowledge about the distribution of erosion rates, which is obtained independently of the absolute values of the ages, can be used to estimate the distribution of ages in each sub-catchment drained by the river. This second piece of information provides additional insight into the spatial distribution of past cooling/erosional events.
In this paper, we present a method that relies on the raw age data only. This avoids any complication or bias that may arise from trying to compare the data to theoretical probability density distributions that rely on a thermal model prediction. We recognize the value of doing so, but thermal models require making assumptions about past geothermal gradients (heat flux) or rock thermal conductivity and heat production, which introduces additional uncertainty in interpreting the data. The first part of this paper describes the method. To demonstrate its accuracy and explore the limits of its applicability, we have applied the method to synthetic age datasets for which we know the erosion rate and its spatial variability. This is done in the second part of the paper. To illustrate the use of our method, we have applied it to a dataset collected in the Himalaya (along the Tsangpo–Siang–Brahmaputra river system). This is explained in the third part of the paper. There we show that the method yields reliable estimates of the distribution of present-day erosion rates in these areas as well as independent information on the spatial extent of past geological events. We conclude by suggesting potential ways in which the method could be improved. Note that the approach proposed here has been used on a set of detrital age distributions collected along the Inn River in the Eastern Alps (Gemignani et al., 2017). Gemignani et al. (2017) showed how age distributions characterized by young age peaks likely produce high estimates of present-day erosion rates when compared with catchments that contain older age peaks.
2 The method
2.1 Basic assumptions
We assume that we have collected a series of age datasets measured at M specific points (or sites) along a river that drains a tectonically active region where the erosion rate is likely to vary spatially. We also assume that the datasets have been used to construct age distributions decomposed into N age bins (see Fig. 1) that may, for example, correspond to given, known geological events or, alternatively, have been selected after constructing a kernel density estimate of the data (Vermeesch, 2012) and applying a mixture model to infer potential discrete age peaks in the age distribution (Sambridge and Compston, 1994). Although each bin corresponds to an age range, it might be easier to refer to it as representative of a given "age", which can be taken as the mean age of the range, for example. We will call Hik for k=1,…,N and i=1,…,M the relative height of bin k in distribution i. Because these are relative contributions, we have
(1)∑k=1NHik=1,for all i=1,…,M.
Figure 1Example of a measured age distribution and the relative heights Hik of the corresponding bins (N=4 in this example).
The landscape is divided into exclusive contributing areas for each of the points along the main river where we have measured a dataset and compiled a distribution from it. We take the convention that Area 1 (of surface area A1) is the area contributing to site 1, whereas Area 2 (of surface area A2) is the area contributing to site 2 but not to site 1. Area i (of surface area Ai) therefore contributes to site i but not to the previous i−1 sites (see Fig. 2). In each Area i, we will assume that αi is the relative abundance of the mineral used to estimate the age distribution in rocks being eroded from the surface. We will call αi the "mineral concentration factor" of Area i. We take the convention that 0<αi<1, with αi=1 corresponding to an area i with surface rocks that contain the mineral in abundance and αi=0 corresponding to an area i with surface rocks that do not contain the mineral. We also call ϵi the unknown present-day mean erosion rate in Area i.
Figure 2Schematic representation of how the landscape is divided into exclusive contributing areas Ai (different shades of gray) for each of the points (here the circles labeled i=1,…,3) along the main river where we have age distributions. ϵi and αi are the assumed mean erosion rate and mineral concentration factor of Area i, respectively.
The surface areas, Ai, can be computed from a digital elevation model. The value of the concentration factors depends on the regional geology. First-order estimates of the αi parameters can be derived by considering the relative occurrence of the specific mineral-bearing rocks in each area from a geological map. However, recent work shows that mineral abundance in river sediments can vary significantly between tectonic units with similar lithology (Malusà et al., 2016). In their work, Malusà et al. (2016) propose a quantitative approach that can be used to infer potential "mineral fertility" bias between adjacent catchments and provide an example from the Alps. It is not the purpose of this paper, however, to speculate on the bias induced by the mineral fertility parameter in the interpretation of detrital age distributions. We refer to previous work such as that of Malusà et al. (2016) or Resentini and Malusà (2012) on the subject and assume that if the necessary data are available to perform a correction for this bias, it should be made by adjusting the value of the mineral concentration factor αi accordingly.
From these simple assumptions, we can write that the number of grains of age k coming out of catchment i is given by
(2)Dik=AiαiϵiCik=FiϵiCik,
where Fi=Aiαi and Cik represents the unknown relative concentration of grains of age k in surficial rocks in Area i. We also have
(3)∑k=1NCik=1,for all i
because Cik also represents relative or normalized concentrations. The relative concentrations, Cik, tell us to what extent age k has been preserved in surficial rocks of Area i, whereas ϵi is a measure of present-day erosion rate in Area i.
2.2 Downstream bin summation along main trunk
We can now write that the predicted height of bin k in the distribution observed at site i should be equal to the total number of grains of age bin k coming from all upstream areas divided by the total number of grains of all ages coming from all upstream areas:
Hik=∑j=1iDjk/∑k=1N∑j=1iDjk(4)=∑j=1iFjϵjCjk/∑k=1N∑j=1iFjϵjCjk.
We can slightly rearrange this to obtain
Hik=∑j=1iFjϵjCjk/∑j=1iFjϵj∑k=1NCjk(5)=∑j=1iFjϵjCjk/∑j=1iFjϵj.
If we divide the numerator and denominator of this expression by F1ϵ1, we obtain
(6)Hik=∑j=1iρjCjk/∑j=1iρj,
(7)ρj=FjϵjF1ϵ1
is the contribution from Area j relative to Area 1. Note that, if we assume that we can confidently estimate Fj=Ajαj, ρj becomes a measure of the unknown erosion rate, ϵj, in Area j relative to the unknown erosion rate, ϵ1, in Area 1.
2.3 Incremental formulation
We now express Eq. (6) as an incremental relationship between Hik and Hi-1k only, i.e., between the relative bin heights of distributions measured at two successive points along the main trunk. From Eq. (6), we can write
Hik=∑j=1iρjCjk/∑j=1iρj=∑j=1i-1ρjCjk+ρiCik/(8)∑j=1i-1ρj+ρi,
and by dividing numerator and denominator by ∑j=1i-1ρj, we obtain
(9)Hik=Hi-1k+δiCik/1+δi,
(10)δi=ρi/∑j=1i-1ρj
is the contribution of Area i relative to the contribution of all upstream areas, i.e.,, what sediment is entering the river from Area i relative to what is already in the river. We will call δi the "relative contribution" from Area i.
We can also write
Hik-Hi-1k=Cik-Hikδi,(11)fori=1,…,Mandk=1,…,N.
From this relationship we see that the relative changes in bin height between two successive sites along the main stream contain information about the present-day erosion rate in the intervening catchment, through the relative contributions δi and the relative concentrations in surface bedrock in Area i, Cik. If one knows either δi or the Cik (for k=1,…,N), one can derive the value of the other quantity/quantities. For example, Resentini and Malusà (2012) assumed that they knew the values of the Cik surface concentrations in each sub-catchment to derive estimates of the corresponding δi. Here we will try to assess whether the values of both the relative surface concentrations Cik and the relative contributions δik can be estimated.
2.4 Estimating erosion rates
Considering that we have M sites along the main river trunk and that we have selected to use N bins to describe the distributions of ages, we have N×M unknown values for the relative heights of the bins describing the distribution of ages in the source areas (the Cik) and another set of M−1 unknown values for the relative contributions δi. However, we only have N×M equations (see Eq. 11), and the problem is clearly underdetermined; i.e., we have more unknowns than equations among the unknowns. This means that there is an infinite number of solutions that satisfy the equations. We cannot estimate the values of all unknowns, but, as we will show now, we can estimate bounds on the value of the unknowns δi.
Two cases must be considered. First, if there is a noticeable change in relative bin heights in the detrital record between sites i−1 and i, i.e., Hik≠Hi-1k, the distribution of ages in Area i, Cik, must be different from that in the river at site i, i.e., Cik≠Hik, and, consequently, the relative contribution from Area i, δi must be finite, i.e., δi>0 and therefore ϵi>0. This means that there must be a minimum finite value for δi and therefore for ϵi to explain the difference in distribution between sites i−1 and i. We will call this the minimum but finite value δim (and ϵim, respectively). There is another solution to consider where δi→∞ and Cik→Hik, which is correct regardless of the values of the Hi-1k and which corresponds to the situation where the relative contribution from Area i is so large that it completely overprints the river signal. We thus have two bounds for the relative contributions δi at each site, one finite and the other infinite:
(12)δi∈[δim,∞].
The second case to consider is when the relative bin heights between two successive sites do not change , i.e., Hik=Hi-1k for all bins k. In this case, we cannot tell if this is because the erosion rate in catchment i is zero (ϵi=0→ρi=0→δi=0) or because the signature of the source in catchment i, i.e., the distribution of ages at the surface, is identical to that of the river (Cik=Hik=Hi-1k), and we have no constraints on δi or the erosion rate in Area i. Although this situation may arise, we will now only consider the case where Hik≠Hi-1k and try to find the value of δim.
For this, we rewrite Eq. (11) as
(13)Cik=Hik-Hi-1kδi+Hik.
Hik represents relative bin heights, i.e., Hik∈[0,1] and ∑k=1NHik=1. This implies that Cik also represents relative bin heights. Therefore we must have ∑k=1NCik=1 and Cik∈[0,1]. This leads to two constraints on the value of the relative contribution δi:
(14)Cik>0→δi>maxk=1,…,NHi-1k-HikHik
(15)Cik<1→δi>maxk=1,…,NHik-Hi-1k1-Hik
for all i=1,…,M. The first condition applies where there is a decrease in any relative bin height k between site i−1 and site i, i.e., Hik<Hi-1k, whereas the second condition applies where there is an increase in any relative bin height k between locations i−1 and i, i.e., Hik>Hi-1k. We can therefore conclude that the minimum values of the contribution factors that are necessary to explain the change in relative bin size from site to site are given by
δim=maxk=1,…,NHi-1k-HikHik,Hik-Hi-1k1-Hik(16)fori=1,…,M.
From these estimates of the contribution factors, δim, we can then derive the value of the corresponding erosion rate in each Area i, ϵim, that is necessary to explain the observed variations in age distributions along the main river trunk, by using the following relationship that we derive in the Appendix:
(17)ϵim=F1ϵ1mFiδim∏j=1i-11+δjmfori=1,…,M,
assuming that δ1m=0 and ϵ1m=1 such that the estimated minimum erosion rates are relative erosion rates scaled by the unknown erosion rate in the first catchment.
From the values of the minimum contribution factors, δim, we can also estimate the relative surface concentrations of each age bin k in each catchment i, using
(18)Cik=Hik-Hi-1kδim+Hik.
3 Using age distributions from tributaries
Age distributions from tributaries can be included to improve the solution locally, i.e., in the tributary catchment. Let us call AT, αt and ϵT the catchment area, the mineral concentration factor and the mean erosion rate of the tributary catchment and AM, αM and ϵM the catchment area, the mineral concentration factor and the mean erosion rate of the rest of catchment i.
For each bin k in the catchment i, we can write
(19)FiϵimCik=FTϵTCTk+FMϵMCMk,
where FT=ATαT and FM=AMαM. By conservation of eroded rock mass, we have
(20)FMϵM=Fiϵim-FTϵT,
which we can use to transform Eq. (19) into
(21)FiϵimCik=FTϵTCTk+(Fiϵim-FTϵT)CMk
to obtain
(22)CMk=FiϵimCik-FTϵTCTkFiϵim-FTϵT.
Using the method for the main trunk data described in the previous sections, we know ϵi and Cik. The tributary data (age distributions) gives us the CTk surface concentrations as measured in the tributary stream (i.e., CTk=HTk) and we can solve for the surface concentrations CMk assuming first that the erosion rate is uniform in the catchment i, i.e., ϵT=ϵM=ϵi, to give
(23)CMk=FiCik-FTCTkFi-FT.
However, this may lead to unrealistic values of the relative surface concentrations CMk, i.e., not comprised between 0 and 1. Consequently, two conditions need to be added so that 0<CMk<1 for all k. The first condition (CMk>0) yields
(24)ϵT<FiCikFTCTkϵim,
while the second condition (CMk<1) yields
(25)ϵT<Fi(1-Cik)FT(1-CTk)ϵim.
The true erosion rate must satisfy both conditions and we therefore select the smallest value of ϵT obtained by considering any relative surface concentration difference between the tributary sub-catchment concentration (CTk) and that of the entire catchment (Cik).
3.1 Uncertainty estimates by bootstrapping
We assess the robustness of our estimates of minimum erosion rate ϵim and corresponding relative concentrations Cik, derived from finite size samples by bootstrapping. For this, we simply use the method described above on a large number of subsamples of the observed distributions constructed by random sampling of the observed distributions with replacement. This yields distributions of erosion rate and relative concentrations that can be used to estimate the uncertainty arising from the finite sample size. These distributions are usually not normal and we use their median value rather than their mean as the most likely estimate of erosion rate and their standard deviation to represent uncertainty.
The code is provided as a Jupyter Notebook containing python code and explanatory notes that refer to the equations given in this paper. The user must provide a series of input files containing (a) the description of the sites, i.e., the order in which the sites are located along the river, whether they drain into the main river stem or into a tributary, the drainage area A, and the mineral concentration factor α, (b) the bin sizes and (c) the observed age data at each site. The code produces distributions of estimates of erosion rate and relative concentration of grains of ages within each bin for each site from the bootstrapping.
4 Assessing the method on synthetic distributions
We have assessed the reliability of the erosion rate estimates obtained from the method by applying it to synthetic age distributions made of N=4 age bins at M=5 sites. We have assumed known mineral concentration factors, αi, contributing areas, Ai, and relative erosion rates. To construct the distributions, we created very large samples (106 grains) from normal distributions of grains having ages centered on four peak values (20, 40, 60 and 80 Myr) with a standard deviation of Δa=5 Myr. The amplitude of the peaks is first set to (0,0,0,1), (0,0,1,0), (0,1,0,0), (1,0,0,0) and (0,0,0.5,0.5), for the five sites. In this way, we are likely to maximize relative bin height differences between two consecutive sites. We will later relax this assumption and see how it impacts the estimates obtained by the method. We then constructed five grain distributions corresponding to each of the sites by adding the original distributions in proportions given by the relative contributions (product of area, mineral concentration factor and erosion rate) of each area. We then sampled these distributions by randomly selecting n=100 grains. This number was chosen because, in many detrital age studies, it represents the average number of grains collected at any given site. We applied our method to these synthetic datasets to obtain distributions of minimum erosion rate by bootstrapping and took the median value as a reliable estimate of the minimum erosion rate in each area necessary to satisfy the synthetic age distributions. We repeated this operation 1000 times (i.e., by generating 1000 synthetic datasets) to obtain the distributions of estimates of minimum erosion rates and compared them to the imposed erosion rates used to construct the synthetic age distributions. In a first set of four experiments, the areas and mineral concentration factors are identical for each site. In the first experiment, the erosion rates are chosen to vary greatly (by up to 2 orders of magnitude) between consecutive sites. In the second experiment, all sites have the same erosion rate; in the third experiment, the erosion rate increases by a factor of 2 from site to site, and in the fourth experiment, the erosion rate decreases by a factor of 2 from site to site. The results are shown in Fig. 3.
Figure 3Results of the method applied to the first set of synthetic datasets. Computed erosion rate distributions for four synthetic datasets. For each distribution, the box extends from the lower to upper quartile values, the line corresponds to the median value, and whiskers extend from the box to show the range of the erosion rate estimates, excluding outliers. Outliers are indicated by small circles past the end of the whiskers. For each site, the gray stars correspond to the imposed erosion rates and the dashed gray line gives the product of the imposed area and fertility factors, Fi=Aiαi.
Figure 4Results of the method applied to the second set of synthetic datasets with random peak amplitudes. See Fig. 3 caption for further details.
We see that the estimated erosion rates (median value) are in good agreement with the imposed (true) erosion rates, especially when large jumps in erosion rate exists between successive sites/catchments. However, in some cases, there appears to be an artificial increase in estimated erosion rate from site to site. This is clearly seen in the case where the imposed erosion rate is assumed to be uniform at all sites or to decrease from site to site. In both cases, the method predicts an apparent (or spurious) increase in erosion rate at the last site.
To determine what controls the reliability of these estimates, we performed two other sets of experiments. The first assumes random amplitudes for the peaks in each of the catchments (Fig. 4), and the second assumes that Fi (product of drainage area Ai by relative abundance αi) increases downstream (Fig. 5). When the amplitudes of the peaks are random, the differences in peak height between two successive sites is much smaller, leading to deteriorated values for the estimated erosion rates. Conversely, when the contributing areas (or relative abundances) increase downstream, the estimates are improved.
Figure 5Results of the method applied to the second set of synthetic datasets with increasing Fi=Aiαi downstream. See Fig. 3 caption for further details.
Table 1Age bins used to construct age distributions shown in Fig. 8 and used in our example.
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Consequently, the accuracy of the estimates of erosion rate obtained from our method rely on whether the two successive age distributions used to estimate the δim are markedly different and whether the size of the two successive catchments increases or, at least, does not decrease substantially. These conditions should be kept in mind when interpreting the results obtained by our method.
Figure 6(a) Location of the study area and (b) location and name of sampling sites and geometry of the drainage basins contributing to each site. The orange shading represents catchments draining directly into the main trunk; pale blue shading represents the tributary catchments or sub-catchments.
5 Applications to a detrital age dataset
To illustrate the method, we now apply it to a detrital age dataset from the eastern Himalaya that contains ages obtained using the muscovite 40Ar/39Ar thermochronometer. The data were combined from published age datasets collected along the main trunk of the Tsangpo–Siang–Brahmaputra river system, as well as along some of its tributaries (Fig. 6), using age bins given in Table 1. Samples A, B, C (composite sample), X and Y are from Lang et al. (2016) and samples Z, T-40a and T-41a are from Bracciali et al. (2016). The complete age datasets are given in the data repository, Table S1 in the Supplement. In Table 2, we give the relative position of the successive samples along the main trunk of the river, i, the respective exclusive contributing areas, Ai, and the mineral concentration factors, αi. We first used constant values of 1 % (fourth column in Table 2). We will also use variable values for the mineral concentration factors, making the simple assumption that surficial rocks have an average mineral composition that depends mostly on their lithology. A very simple way to proceed is to look at the surface lithology indicated in the geological map of the region for each studied catchment. In this case we used the geologic map of the eastern Himalaya from Yin et al. (2010). The semi-quantitative α values computed for each catchments are given in the fifth column of Table 2. We are aware that, for such a complex and vast area, this approximation can yield poor estimates of the concentration of the target mineral in surface rocks. For more accurate methods (that also require additional data), we refer the user to published work by others that have investigated this issue by looking, for instance, at petrographic and heavy mineral density in modern river sediments, such as the SRD (Source Rock Density) index (Garzanti and Andò, 2007) or the method proposed to compute mineral fertility (Malusà et al., 2016).
We will investigate the effect of using variable concentrations of muscovite-bearing rocks in each of the catchments as derived from a geological map of the area (fifth column in Table 2).
Bracciali et al. (2016)Bracciali et al. (2016)Lang et al. (2016)Lang et al. (2016)Lang et al. (2016)Lang et al. (2016)Lang et al. (2016)Bracciali et al. (2016)
Table 2Relative position along the main trunk of the Tsangpo–Siang–Brahmaputra river system. Negative numbers indicate samples collected along a tributary. Catchment areas and mineral concentration factors used to compute the erosion rate reported in Table 7. The two columns correspond to two different sets of values used for comparison. Site names refer to locations shown in Fig. 6.
Table 3Predicted erosion rate in the successive sub-catchments obtained by assuming uniform values for the αi and variable values derived from the relative abundance of muscovite-bearing surface rock in the geological map.
Figure 7Computed erosion rate distributions obtained by applying the method to a Himalayan dataset. For each distribution, the box extends from the lower to upper quartile values, the line corresponds to the median value, and whiskers extend from the box to show the range of the erosion rate estimates, excluding outliers. Outliers are indicated by small circles past the end of the whiskers. Erosion rate values are normalized such that the mean is 1. The gray circles connected by a dashed gray line are the product of the imposed area and mineral concentration factors, Fi=Aiαi, at each site. Site names refer to locations shown in Fig. 6.
Figure 8Observed distributions of ages (light gray bars) in samples collected at sites shown in Fig. 6 on which the distributions of predicted surface age distributions have been superimposed for each site. For each distribution, the box extends from the lower to upper quartile values, the line corresponds to the median value, and whiskers extend from the box to show the full range, excluding outliers. Outliers are indicated by small circles past the end of the whiskers.
Figure 9Maps of predicted median erosion rates (central panel) and relative surface age concentrations from the muscovite detrital data from eastern Himalaya. See Figs. 8 for full distributions and data.
Results are shown in Fig. 7 as distributions of computed relative minimum erosion rates (i.e., normalized such that the mean erosion rate is 1) obtained by bootstrapping. The computed surface concentrations, Cik, for each site i are shown in Fig. 8. Figure 9 contains maps of the various catchments shaded according to their predicted median erosion rate and surface concentrations of grains of age within each range. Predicted concentrations are scaled such that the sum of the five age bin concentrations is 1 in each catchment. We see that predicted minimum erosion rates increase by about 2 orders of magnitude with distance along the main river trunk from its source area along the southern margin of the Tibetan Plateau. Maximum erosion rates are observed in catchment C (and sub-catchment X), which is closest to the eastern Himalayan syntaxis. The amplitude of the jumps in erosion rate between sites A and B and between B and C are potentially amplified by our method because sites A, B and C have relatively small areas, Ai, and contain relatively uniform distributions of ages among the five bins. As we have noticed in our synthetic examples, this may lead to a spurious increase in erosion rate. Further downstream (catchment Z), the predicted minimum erosion rate remains high but lower than observed near the syntaxis. This estimate is likely to be robust as site Z has a very large contributing area and has an age distribution that is markedly different from that of the previous catchment (C).
The most salient result predicted by the method is that the erosion rate in the eastern Himalayan syntaxis should be at least 5–7× higher than the mean erosion rate along the Tsangpo–Siang–Brahmaputra basin. This estimate is in good agreement with the conclusions of Stewart et al. (2008), who used U-Pb ages of detrital zircon grains from the Brahmaputra River to demonstrate that approximately half of the sediment flux carried by the Brahmaputra River originates from an area around the eastern syntaxis that represents only 2 % of the total area of the river drainage basin. This implies that erosion rate in the vicinity of the syntaxis should be approximately 25× higher than the mean erosion rate. Similar estimates were obtained by Enkelmann et al. (2011) using a larger detrital age dataset from the area, while a relatively smaller estimate (erosion rate should only be 5× higher than the mean in the syntaxis area) was obtained by Singh and France-Lanord (2002) using the isotopic composition of sediments collected along the Brahmaputra River. In Table 3, we compare the erosion rate estimates obtained using uniform surface concentration factors (αi=0.01) with those obtained using variables values (given in the fifth column of Table 2). We note that although the values obtained for the sub-catchments upstream of the syntaxis (sites Y and B) are somewhat reduced, the very large relative erosion rates (5–8× the entire basin average) predicted in the syntaxis (sites X and C) are a robust outcome of the method.
We also note that erosion rates in sub-catchments Y and X do not need to be noticeably smaller than the estimates of erosion rate in their host catchments (B and C). As explained earlier, the true erosion rates could be larger than those of their host catchments. This could be the case for sub-catchment Y, where we predict an erosion rate identical to that of catchment B. These estimates are likely to be reliable because their area is similar to that of their sub-catchment (they occupy a non-negligible portion of their host catchment) and because they have strikingly different age distributions than their host catchments (B and C; Fig. 8). One can also see from the age distributions shown in Fig. 8 how the large contribution from bin 4 at site Y affects the relative height of bin 4 at site B and, similarly, how the large contribution from bin 3 affects the relative height of bin 3 at site C.
Interestingly, there is a good correspondence between present-day erosion rate and where the youngest ages are being generated (sites B and C), with the notable exception of the most downstream catchment (Z). In other words, where the mixing analysis predicts a high minimum erosion rate to account for a substantial change in the age distribution between two adjacent catchments is also where it predicts the highest concentration of young ages in the surface rocks. At the downstream end of the river (Catchment Z), we predict a relatively high minimum erosion rate from the mixing model but a relatively low concentration of young ages in comparison to the other catchments. This could mean that, in catchment Z, the present-day high erosion rate is relatively recent and has not yet led to a complete resetting of cooling ages which were set during earlier events.
Figure 10Relative uncertainty in erosion rate scaled by the relative uncertainty in mineral concentration factor for the estimates obtained at each of the six sites along the main river trunk. The first site has a fixed relative erosion rate (=1) and therefore no uncertainty.
We also note that the difference between the two quartile values (vertical size of the boxes in Fig. 7 and 8) is large, of the order of 50–100 % of the median value of predicted erosion rate values. This indicates that our method can only provide order-of-magnitude estimates of the minimum erosion rate necessary to explain the age distributions. Interestingly, the difference between the two quartile values does not increase downstream, which demonstrates that the uncertainty introduced by using incomplete or nonrepresentative subsamples of the true distributions at each of the station does not accumulate as our algorithm proceeds from station to station. This results from the incremental nature of our algorithm, as shown by Eq. (11).
6 Mineral concentration factors and their uncertainty
One of the sources of uncertainty in our estimates of the erosion rate comes from the assumed value of the mineral concentration factors, αi, which might be difficult to estimate in many situations (Malusà et al., 2016). We can compute the uncertainty on the erosion rates, Δϵi arising from the uncertainty on the mineral concentration factors, Δαi, from
(26)Δϵi=∑k=1i∂ϵi∂αk2Δαk2,
(27)∂ϵi∂αk=0ifk>iϵiαiifk=iδimFiAkϵk+∑j=1i-1Fj∂ϵj∂αkifk<i.
The results are shown in Fig. 10 as a plot of the ratio between the relative uncertainty in estimates of erosion rate Δϵi∕ϵi and the relative uncertainty in mineral concentration factors, Δαi∕αi, for the six stations located along the main river trunk. We see that the relative uncertainty in erosion rate is approximately proportional to the relative uncertainty in mineral concentration factor (i.e., all values are close to 1) and that there is only a minor downstream propagation of the uncertainty. This is also a simple consequence of the incremental nature of our algorithm, as explained by Eq. (11).
7 Ways in which the method could be improved
As described in this paper, our methods relies on the existence of age clusters (or bins) that can be found in the age distributions collected at various sites along a river. The method could be generalized by constructing kernel density estimates of the distribution of ages at each sites. These could be used to estimate the minimum contribution factors, δim, by applying the condition of Eq. 16 for the complete range of ages, not just the discrete values obtained by binning. We have found, however, that the solution we obtain in this way is strongly dependent on the choice made for the kernel and further investigation of this issue is required. Alternatively, estimates of cumulative density functions could be built directly from the data and, in turn, used to impose condition (Eq. 16) and estimate the minimum contribution factors, δim. However the limited number of grains at each site makes the comparison between two successive CDFs (cumulative density functions) rather inaccurate. The accuracy of this approach can be tested by increasing the number of bins in our model such that N becomes similar to the average number of grains in any dataset. This leads to an overestimation of the minimum contribution factors. Clearly more work is required to investigate better or alternative ways to compare two successive age distributions.
8 Conclusions
We have developed a simple method to extract spatially variable erosion rates and surface age distributions from detrital cooling age datasets from modern river sands. The method is based on what we believe are the simplest assumptions necessary to interpret such data and does not rely on a priori knowledge of the distribution of ages in surrounding catchments. In describing the method we have demonstrated that it is suited to extract two apparent sources of information pertaining to the spatial distribution of erosion rate along the river. The first comes from using the age distributions as fingerprints characterizing the areas between two successive sites where detrital samples were collected. This allows us to predict first-order estimates of the relative erosion rate between these areas and the distribution of ages in surficial rocks in each area. These estimates of age distributions can be used as a second independent information on the past and present erosion rate in each area.
By applying the method to an existing dataset from the eastern Himalaya, we show that the method provides estimates of present-day erosion rate patterns in the area that is consistent with previous, independent estimates, potentially evidencing that the fast present-day erosion rates in some parts of the study area are relatively young. We stress, however, that our method can only provide reliable estimates of erosion rate when the age distributions observed at two successive sites are different.
Importantly, the method is limited to providing the spatial distribution of erosion rate; independent information is necessary to transform those into absolute estimates of erosion rate.
Code and data availability.
We provide a simple implementation of the method in python within a Jupyter Notebook and the data collected from published sources and used in this paper for illustration purposes.
From the definition of the relative contributions, δi,
(A1)δi=ρi∑j=1i-1ρjfori=2,…,M,
(A2)ρi=FiϵiF1ϵ1,
we can write
Fiϵi=δi∑j=1i-1Fjϵj=δi∑j=1i-2Fjϵj+Fi-1ϵi-1=δi∑j=1i-2Fjϵj+δi-1∑j=1i-2Fjϵj(A3)=δi(1+δi-1)∑j=1i-2Fjϵj.
By performing this operation i−2 times and arbitrarily setting δ1=0, we obtain
(A4)Fiϵi=δi∏j=1i-1(1+δj)F1ϵ1fori=2,…,M.
Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/esurf-6-257-2018-supplement.
Competing interests.
The authors declare that they have no conflict of interest.
Acknowledgements.
The work leading to the results presented here was supported by the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/People/2012/ITN, grant agreement number 316966.
Edited by: Sebastien Castelltort
Reviewed by: Mark Brandon and Marco Giovanni Malusà
Bernet, M., Brandon, M., and Garver, J. I.: Downstream changes of Alpine zircon fission-track ages in the Rhône and Rhine Rivers, J. Sediment Res., 74, 82–94, 2004. a, b
Bracciali, L., Parrish, R. R., Najman, Y., Smye, A., Carter, A., and Wijbrans, J. R.: Plio-Pleistocene exhumation of the eastern Himalayan syntaxis and its domal "pop-up", Earth Sc. Rev., 160, 350–385, 2016. a, b, c, d
Brandon, M.: Decomposition of fission-track grain-age distributions, Am. J. Sci., 292, 535–564, 1992. a
Braun, J.: Strong imprint of past orogenic events on the thermochronological record, Tectonophysics, 683, 325–332, 2016. a
Brewer, I. D., Burbank, D. W., and Hodges, K. V.: Downstream development of a detrital cooling-age signal: Insights from 40Ar/39Ar muscovite thermochronology in the Nepalese Himalaya, in: Special Paper 398: Tectonics, Climate, and Landscape Evolution, Geological Society of America, 321–338, 2006. a, b
Brown, R.: Backstacking apatite fission track "stratigraphy": a method for resolving the erosional and isostatic rebound components of tectonic uplift histories, Geology, 19, 74–77, 1991. a
Enkelmann, E. and Ehlers, T. A.: Evaluation of detrital thermochronology for quantification of glacial catchment denudation and sediment mixing, Chem. Geol., 411, 299–309, 2015. a
Enkelmann, E., Ehlers, T. A., Zeitler, P. K., and Hallet, B.: Denudation of the Namche Barwa antiform, eastern Himalaya, Earth Planet. Sc. Lett., 307, 323–333, 2011. a
Garzanti, E. and Andò, S.: Heavy mineral concentration in modern sands: implications for provenance interpretation, Elsevier, Amsterdam, 567–598, 2007. a
Gemignani, L., Sun, X., Braun, J., van Geerve, D., Thomas, R., and Wijbrans, J.: A new detrital mica 40Ar/39Ar dating approach for provenance and exhumation of the Eastern Alps, Tectonics, 36, 1521–1537, 2017. a, b
Gleadow, A. and Brooks, C. K.: Fission track dating, thermal histories and tectonics of igneous intrusions in East Greenland, Contrib. Mineral. Petr., 71, 45–60, 1979. a
Kellett, D. A., Grujic, D., Coutand, I., Cottle, J., and Mukul, M.: The South Tibetan detachment system facilitates ultra rapid cooling of granulite-facies rocks in Sikkim Himalaya, Tectonics, 32, 252–270, 2013. a
Lang, K. A., Huntington, K. W., Burmester, R., and Housen, B.: Rapid exhumation of the eastern Himalayan syntaxis since the late Miocene, Geol. Soc. Am. Bull., 128, 1403–1422, 2016. a, b, c, d, e, f
Malusà, M. G., Resentini, A., and Garzanti, E.: Hydraulic sorting and mineral fertility bias in detrital geochronology, Geol. Rundsch., 31, 1–19, 2016. a, b, c, d, e
McPhillips, D. and Brandon, M.: Using tracer thermochronology to measure modern relief change in the Sierra Nevada, California, Earth Planet. Sc. Lett., 296, 373–383, 2010. a
Resentini, A. and Malusà, M.: Sediment budgets by detrital apatite fission-track dating (Rivers Dora Baltea and Arc, Western Alps), Geol. Soc. Am. Spec. Pap., 487, 125–140, 2012. a, b, c
Ruhl, K. W. and Hodges, K. V.: The use of detrital mineral cooling ages to evaluate steady state assumptions in active orogens: An example from the central Nepalese Himalaya, Tectonics, 24, TC4015, https://doi.org/10.1029/2004TC001712, 2005. a
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We present a new method to interpret a type of data that geologists obtained by dating minerals in river sand samples. We show that such data contain information about the spatial distribution of the erosion rate (wear of surface rocks by natural processes such as river incision, land sliding or weathering) in the regions neighboring the river. This is important to understand the nature and efficiency of the processes responsible for surface erosion in mountain belts.
We present a new method to interpret a type of data that geologists obtained by dating minerals...


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Issued weekly by the City of Diamond Bar - January 20, 2023
Welcome to Weekly Highlights from City Hall - Each issue of this electronic bulletin will contain brief news and information about City programs, services and projects.
At this meeting, City staff and project consultants will:
provide an update on the preparation of the Specific Plan, and
seek direction on the buildout and design parameters for the project, such as maximum number of residential units, commercial square footage and building heights
Architectural styles and inclusionary housing will also be discussed at this meeting.
This is an open meeting and the public is invited to attend in person or remotely using the GotoWebinar videoconferencing platform
Attend In Person: City Hall - Windmill Community Room, 21810 Copley Drive
Join on your computer or other mobile device, or call in (audio only): The GotoWebinar link and call-in phone number will be provided as part of the meeting agenda, which will be posted in advance of the meeting at www.diamondbarca.gov/meetings.
Community Development Department
New Businesses: January 11-17
La Vie Nails & Spa
1120 S. Diamond Bar Boulevard
P-Duke Technology, Inc.
717 Brea Canyon Road, Unit 1
Wholesaler of electronic components
Registration Now Open for Youth Indoor Soccer
This 8-week program is designed to teach the skills and fundamentals of soccer to children ages 3-8 in a fun, safe, and recreational environment. Practices and games will be played at Mt. Calvary Lutheran School on Saturdays between 8 a.m. and 5 p.m., from April 1 through May 20, 2023. Registration is $125 per participant for Diamond Bar residents and $130 per participant for non-residents. For more information call (909) 839-7070 or register online at www.DiamondBarCA.gov/recregister
Excursion to Fantasy Springs Casino
Sign up now for the senior excursion to Fantasy Springs Casino on February 28 for a chance to win big! Everyone must present a valid California ID to play. Get $35 of free play on your player's card plus $5 for food. Diamond Bar residents may sign up for $35 per person and non-residents for $40 per person. The excursion includes roundtrip transportation, players card, and a snack on the way home. Register online or in person at the Diamond Bar Center. Call (909) 839-7068 for more information.
Join us for the next Coffee with a Cop event!
January 24, 2023 | 5:30 p.m. 7:30 p.m.
The Mugs
20657 Golden Springs Drive, Ste. 101, Diamond Bar, CA
For more events, visit https://www.diamondbarca.gov/865/Coffee-with-a-Cop
Town Center Specific Plan Joint City Council and Planning Commission Meeting | January 24
Parks and Recreation Meeting | January 26
City Council Meeting | February 7
Meeting participation is available in person and online. Participation details are available on the first page of every meeting agenda. Learn more and view archived City meetings at www.diamondbarca.gov/agendas.
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redpajama_set_name: RedPajamaCommonCrawl


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Lo schiavista (The Sellout) è un romanzo dello scrittore statunitense Paul Beatty, pubblicato per la prima volta nel 2015.

Trama
Ambientato a Los Angeles, il romanzo segue la storia di un uomo che coltiva angurie e marijuana. Il narratore, noto con il soprannome di "Bonbon Me", cerca di reintrodurre la segregazione razziale negli Stati Uniti e comincia con il possedere uno schiavo a Dickens, il suo quartiere di Los Angeles. Il caso arriva alla Corte Suprema con il nome "Me Vs. The United States of America".

Accoglienza
Il romanzo è stato accolto positivamente dalla critica britannica e statunitense, che ne ha lodato il contenuto satirico. The Guardian ha definito Beatty il più divertente degli scrittori statunitensi. Tuttavia, lo scrittore ha espresso sorpresa quando il suo romanzo è stato definito comico, specialmente dato che secondo lui questa etichetta ha ostacolato un'analisi più approfondita delle tematiche trattate nel suo romanzo.

Il romanzo è valso a Beatty il Man Booker Prize e il National Book Critics Circle Award. Beatty è diventato così il primo scrittore statunitense a vincere il Man Booker Prize.

Edizioni

Note

Collegamenti esterni

Romanzi di autori statunitensi
Romanzi ambientati a Los Angeles
Letteratura afroamericana

redpajama_set_name: RedPajamaWikipedia


HY chances this late in the game.
Non-traditional Undergrad - Law School Admin Chance?
3.1 GPA, where am I locked out?
3.2 LSAC GPA - T50 schools for IP?
Minimum LSAT for T14 with my GPA?
Is my school list good (AA MALE URM) any safeties to add???
Chances at UNC, Ohio State?
Too late to apply for SLS?


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Paris: Top 10 locations for Sex and the city
Sex and the city of Paris
Sex and the city is an emblematic series in New York, but is just as emblematic in Paris. Find these places and many others in our Paris guide of the 1000 cult places of films, series, music, comics and novels.
At the lunch between Carrie (Sarah Jessica Parker) and Juliette (Carole Bouquet), her fiancé's ex-wife, opinions are divided about the location. For the Frenchwoman, the chairs are "hideous" while the American thinks that the Kong is one of the most chic and Parisian places there is. Summum of glamour, the Kong is located at the same address as the Kenzo bubble. A world apart.
Discover this fanspot by clicking here
Hotel Plaza Athénée
In the bar of this five-star hotel, home to the loves of Marlene Dietrich and Jean Gabin, Aleksandr Petrovsky (Mikhail Baryshnikov) offers Carrie an impressive diamond jewel. But their relationship quickly deteriorates and during the separation argument in their bedroom, the piece of jewellery breaks and the diamonds scatter on the floor. When Carrie goes down to the reception desk, she runs into Mr Big (Chris Noth) in the lobby and makes a fresh start in his company.
Dior is one of the must-see addresses for fashionista Carrie. But she would have liked to have made a less conspicuous entrance to the Champs-Elysées shop. Going there on a rainy day, the water dripping from her red umbrella forms a puddle over which the young woman slides. She then spreads herself out all over the place with her most Frenchy "bonjour".
Sitting at a table in this chic restaurant, Carrie observes with great nostalgia four young women not far from her, reminding her very much of her small group in New York. As we mentioned in our guide Paris des 1000 lieux cultes de films, series, music, comics and novels, the establishment is located on the avenue de la capitale with the most luxury boutiques as well as the hotel Le Plaza-Athénée where the heroine resides during her Parisian stay.
Place Saint-Sulpice
Carrie calls Miranda (Cynthia Nixon) from a grey and blue payphone to tell her that life in Paris is not as beautiful as she expected. Behind it is the splendid fountain erected from 1843 to 1848 by the architect Louis Visconti. Don't look for the phone booth, it had been installed especially for the stage by the production.
Jeu de Paume Museum
Overwhelmed by his exhibition, Aleksandr Petrovsky ignores Carrie all evening. Located in the Tuileries garden, this 20th and 21st century image art centre hosts numerous exhibitions on the 1,200 m2 of this building built in 1861 to house the courts of the jeu de paume, the ancestor of tennis.
Carrie moved to Paris with Aleksandr Petrovsky, her Russian lover. Mister Big meets her again and finally, on the Pont des Arts, declares to her, "Carrie, you're the one". What could be more romantic than such a statement on this famous bridge between the emblematic Palais du Louvre and the Institut de France?
Place Dauphine
Carrie and Aleksandr live a real moment of joy on this square embodying the romantic and luxurious Paris that the American dreams of. She is then happy that her book is selling so well in France, while she is excited about her new exhibition. Place Dauphine regularly appears in films but also in songs such as Il est cinq heures, Paris s'éveille by Jacques Dutronc or Nous nous sommes tant aimés by Yves Simon.
Via Servandoni
Walking between the Luxembourg Gardens and the Place Saint-Sulpice, Carrie soaked her white pumps in dog excrement and washed them with a nearby water fountain. She's not the first heroine to go down that road. In Alexandre Dumas' novel Les Trois Mousquetaires, the character of d'Artagnan lives at 7 rue des Fossoyeurs (now 12 rue Servandoni) and Marius Pontmercy, a character in Victor Hugo's Les Misérables, lived at 7 rue des Fossoyeurs (now 12 rue Servandoni) from 1817 to 1827.
This establishment no longer exists: the old pastry shop has been replaced by Cojean, organic coffee, and the interior has been completely redecorated. The view on the back of the Louvre is splendid. In the show, Carrie sits next to a big dog and… smoking a cigarette inside. As a nod to history, a café called Le Fumoir is just a stone's throw away, at number 6.
The Fantrippers Buying Board
The Paris guide to the 1000 cult places of films, series, music, comics and novels
The coolest guide in Paris!
The café of Amélie, the mansion of Untouchables, the jazz club of the finale of La La Land, the Hôtel du Nord of the mythical replica of Arletty "Atmosphere", the cinema of A bout de souffle, the restaurant of Ratatouille, the quays of the Seine of Midnight in Paris, the secret places of the Da Vinci Code, the grocery store of January in La Traversée de Paris , the Bridge of Inception and Peur sur la ville, the addresses of the spectacular scenes of Mission Impossible 6, but also series Call my agent, Gears, Le Bureau des légendes, Sense8, Sex and The City, Gossip Girl…
Etienne Daho's Café de Flore, Jay-Z and Kanye West's hotel in Nas in Paris, Serge Gainsbourg's Poinçonneur des Lilas metro station, Serge Reggiani and Marc Lavoine's Mirabeau Bridge, Mc Solaar's Lyon station…
The places evoked in the comics Adèle Blanc-Sec, Largo Winch, Blake and Mortimer, Michel Vaillant…
But also in the novels of Ernest Hemingway, Victor Hugo, Leo Malet, Daniel Pennac, Marc Levy, Guillaume Musso… you'll find all the must-see places of Parisian Pop Culture in this new guide.
Buy at the best price on:
Fantrippers' opinion
Interest for fans
By Anthony Thibault
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We pride ourselves on being the #1 provider of marketing lists in the UK especially if due to GDPR you only want to contact Limited Companies.
Number of Limited Companies: 94,639** Every record includes at least one email address.
Number of Business Categories: 1700+ See the number of Email Addresses for each Business Category here.
Total Number of Email Addresses: 119,627** More than one email address contact at each business is often provided.
Records with a Full Postal Address: 95 % of records.
Telephone Numbers: 78% of records.
Note: Details of Business that are not Limited Companies are NOT included. Also to avoid including any personal email addresses, free type email addresses (such as Gmail, Yahoo, Hotmail etc) as well as email addresses at telecoms services (such as BT, Virgin etc) are NOT included . We only include email address domains in this List that match the business's website URL.
Note : All email addresses were last checked during January 2019.
The buyer just needs to buy the hourlie / gig and he/she gets the delivery within 24 hours. That's it. Enjoy!!!
I hate leaving negative reviews but on this occasion, I have to say I was disappointed. Out of nearly 74k data, we only found 3,228 emails to be valid. VERY DISAPPOINTED!
Data Invalid, around 94 Worthless, Do not Buy, He said would check 12 days later NO REPLY.
Delivered promptly and the data I have examined seems up to date and accurate.
Very quick to provide, data dated but ample amount with relevant address and number.
Thank you.. you're always welcome..


Oh how we take for granted the magically clean clothes, sheets and towels that come out of advanced machines that do everything but fold and put things away. Today's appliances virtually think for themselves – which has left most of us pretty clueless on how to get things clean without them.
Your first and most basic option is using a tub, sink or 5-gallon bucket. Many people love the Hand Operated Washing Machine, ($14.99) which looks sort of like a plunger but has vents. The point of using a (clean!) plunger or the Hand Operated Washing Machine is to push and pull soapy water through the fabric without wearing out your clothes. Use 2 gallons of warm water, 1 tablespoon of homemade laundry detergent or commercial detergent and ½ cup of white vinegar (to brighten whites, cut oils and preserve colors). Plunge for 2 minutes, rinse, wring and hang to dry.
For laundry, you can use clean water from a rain barrel, snow melt, lake or stream (but not saltwater). This water doesn't have to be so clean that it's potable but it should be clean enough that it won't leave your clothes dirtier or smellier than before the washing.
Now, just in case you don't have your grandma's old wringer washer sitting around and you want to get fancier than a 5-gallon bucket, there are alternatives. For example, MIT has developed the GiraDora, a pedal-powered washing machine that looks much like a beverage dispenser cooler. The Up-Stream has the ability to "spin" clothes and the Wonder Wash is a commercially available option already being used in dorms and small urban apartments all over the U.S.
Don't wait until the lights go out to figure out a plan for clean clothes, sheets and blankets. Basic hygiene is right at the top of the list for long-term survival – and short-term comfort.


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Q: Как запомнить переменную? Добрый день! При первом заходе на страницу выбирается вкладка номер 1 (инициализация). Дело осложняется тем, что на странице сделан пагинатор, который перезагружает страницу
(он-то сделан в виде гиперссылок).
 СОРТИРОВКА ПО :
Закладка 1
Закладка 2
Закладка 5
//Инициализация

Мне нужно как-то запомнить номер выбранной закладки. А то всё время после перезагрузки получается отрабатывает секция инициализация и, соответственно, выбирается закладка №1.
Перенесено из ответа.
Дело в том, что пагинация завязана на PHP и MySQL. Номер вкладки определяет способ сортировки результирующей таблицы. Грубо говоря, SELECT * from table ORDER BY 'номер_вкладки'. Так что одним JavaScript не обойтись. PHP-пагинатор вызывает перезагрузку страницы, но всё время выдаётся первая вкладка.
А как использовать глобальные переменные в JS, нигде нормально про это не рассказано? И ещё вопрос, можно ли получить адрес страницы путем JS или JQuery?
A: Вариант А: использовать анкоры. То есть, задавать href в виде "#0", "#1" и т.п., а потом разбирать адрес страницы и открывать нужную вкладку.
Вариант Б: использовать cookies. Тем более, что для них уже написаны удобные функции.
A: А я все такие вкладки-пагинацию полностью на аяксе делаю, нужные страницы храню в глобальных переменных javascript - и всё отлично работает как часы :)
А если переделывать не хочется - то варианты решения вам подсказал товарищ @ling

redpajama_set_name: RedPajamaStackExchange


This was done on a Velvet Gray Strathmore paper I've been experimenting with...pastels used were NuPastels, Terry Ludwigs and Unison's.
It just amazes me Mike how you have that beautiful looseness to your work, yet it looks so real. Amazing! Love this.
This reminds me of all the paintings you see in a Harley Brown book.
paula Thanks..guess I forgot to add the size...it's approx. 9" x11" This was a fun paper to work on. I've been trying some different surfaces and really enjoyed this. I noticed Alicia had done one on suedeboard (sp?) ...I've never used that but thought it might also be fun!
Great as usual. Have you ever considered doing a workshop in Rochester, NY??
Mike, I just love it. Your style is just exactly my goal. How about are workshop in North Carolina too!!!!!
Wonderful work as always! Thanks for sharing...it's always a treat.
Looks like a brush painting. Wish I could paint like that.
How do you like the Strathmore paper, Mike? I mean, obviously it agrees with you :thumbsup: but do you plan to continue with it? How do you think it compares to the Canson MT?
These are all so good, Mike.
Appreciate all the kind comments!
"How do you like the Strathmore paper, Mike? I mean, obviously it agrees with you but do you plan to continue with it? How do you think it compares to the Canson MT?"
I first read about this paper in William Maugham's book Artist's Complete Guide to Drawing the Head.
The Strathmore paper is much thinner and has a smoother surface than the Canson but I was surprised as to how well it handles the pastel. I don't think it would take the rough treatment that Canson can take but for something that's direct and doesn't require a lot of correction it works fine. I plan on using it not only for quick color studies but for the quick portrait sketches with the Carbothellos.
Attached a couple of the sketches where I used just the Carbothello red and white pastel pencils. The warm tone of the paper acts as a great middle tone.
Oh Mike, these are just beautiful! The looseness of your mountain man is incredible. The shapes and values are wonderfully rendered. Absolutely beautiful work!
Thanks Tracer for the nice comment!
I do know I like the smoother side of it to work on (like the Canson) and I don't noticed the pattern as much...now that you mentioned it, a while back it used to really bother me, not sure of when that changed.
Mike your mountain main is wonderful, totally your style, and the drawings are wonderful as well, more classical... what a great artist you are!
As for William Maughan book, is it interesting? I had thought about buying it, last year,then I didn't; do you think it's worth it?
I appreciate both your styles. The colours and pastel work in mountain man are playful and lively. LOVE IT!
The other two portraits are excellent. Great work!


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text:
Provide JSON-LD support for Wikidata
cscott
Lea_Lacroix_WMDE
Oct 16 2018, 12:59 PM2018-10-16 12:59:23 (UTC+0)
MediaWiki-extensions-WikibaseRepository (Backlog)
Wikidata (in progress)
Wikidata-Campsite (Wikidata-Campsite-Iteration-∞ (On Hold)) (Done)
Patch-For-Review
_jensen
abian
Addshore
AnjaJentzsch
View All 28 Subscribers
"Party Time" token, awarded by dbarratt."Like" token, awarded by abian.
operations/mediawiki-config master +0 -9 Enable jsonld output format for wikibase entities everywhere
Open None T44063 [Epic] Provide a plain linked data interface for accessing entities
Resolved cscott T207168 Provide JSON-LD support for Wikidata
Resolved Ladsgroup T226472 JSONLD not recognized as a valid output format for entity data on Beta Wikidata
Open None T227712 Set .json-ld as an alias of .jsonld
T226472: JSONLD not recognized as a valid output format for entity data on Beta Wikidata
Lea_Lacroix_WMDE triaged this task as Medium priority.Oct 16 2018, 12:59 PM2018-10-16 12:59:23 (UTC+0)
Lea_Lacroix_WMDE created this task.
Restricted Application removed a project: Patch-For-Review. · View Herald TranscriptOct 16 2018, 12:59 PM2018-10-16 12:59:24 (UTC+0)
Lea_Lacroix_WMDE added a comment.Oct 16 2018, 1:06 PM2018-10-16 13:06:04 (UTC+0)
JsonLD is now enabled on beta, see announcement
Addshore moved this task from incoming to in progress on the Wikidata board.Oct 16 2018, 1:50 PM2018-10-16 13:50:13 (UTC+0)
Tpt added a comment.Edited · Oct 16 2018, 2:39 PM2018-10-16 14:39:38 (UTC+0)
Not a problem but a cosmetic proposal: Instead of having the structure:
"@graph": [
"@id": "wdata:Q64",
"@type": "schema:Dataset",
"about": "wd:Q64",
...a
"@id": "wd:Q64",
"@type": "wikibase:Item",
...b
"@context": ...c
it would be nice to output:
The two structures encodes exactly the same RDF graph but the new one would allow to get the item by using the .about path instead of .@graph[1] path that is more fragile.
daniel added a comment.Oct 16 2018, 3:43 PM2018-10-16 15:43:24 (UTC+0)
@Tpt
If I understand you correctly, structure you propose would fail if there is more than one entity, though. Which is typically indeed the case, since we return stub representations of referenced entities along with the data about the requested entity.
Tpt added a comment.Oct 16 2018, 3:55 PM2018-10-16 15:55:31 (UTC+0)
@daniel We could do something similar for stubs. with structures like:
"property": {
"label": { "@value": "Foo", "@language": "en"}
"property": "wd:Q64",
This structure may make the client processing more convenient and the structure look more like a "usual" JSON REST API.
But my proposed structure is indeed forbidding to return the description of unrelated entities.
cscott added a comment.Oct 16 2018, 7:52 PM2018-10-16 19:52:49 (UTC+0)
We only emit the "@graph" form in purtle if the API is used to annotate more than a single entity: https://github.com/wikimedia/purtle/blob/master/src/JsonLdRdfWriter.php#L37
So you're not really asking for a change in the JSON-LD, you're asking for wikidata to only emit a single entity on the /entity/* endpoint -- and that would/could apply to all the different representations using the purtle backend. That's not a JSON-LD specific request.
Not really, in my proposal there is still the same number of RDF resources described (in my first example at least wdata:Q64 and wd:Q64). I proposing a new Purtle feature that would allow to make use of JSON-LD embedding feature [1] and probably also of the nested rdf:Description tags of RDF/XML and the short blank node syntax of Turtle [2]. But it's indeed probably out of scope of this task. I'm going to open a new task instead.
[1] https://www.w3.org/TR/json-ld/#embedding
[2] https://www.w3.org/TR/turtle/#unlabeled-bnodes
daniel added a comment.Oct 17 2018, 11:45 AM2018-10-17 11:45:22 (UTC+0)
@Tpt embedding will be hard to do due to the streaming nature of purtle. But let's discuss that when you file the new ticket.
Salgo60 added a subscriber: Salgo60.Oct 17 2018, 1:05 PM2018-10-17 13:05:47 (UTC+0)
Christopher added a subscriber: Christopher.Oct 23 2018, 2:41 PM2018-10-23 14:41:22 (UTC+0)
according to mailing list (Wikidata Digest, Vol 83, Issue 18), this now enabled on beta. Yet when one requests the link: https://wikidata.beta.wmflabs.org/wiki/Special:EntityData/Q64.jsonld, it does not work?
Smalyshev added a comment.Oct 23 2018, 11:22 PM2018-10-23 23:22:50 (UTC+0)
This works though: https://test.wikidata.org/wiki/Special:EntityData/Q64.jsonld
Christopher added a comment.Edited · Oct 24 2018, 12:39 AM2018-10-24 00:39:28 (UTC+0)
Liuxinyu970226 removed projects: Tracking-Neverending, Epic.Oct 25 2018, 1:24 PM2018-10-25 13:24:19 (UTC+0)
Why these two? I don't see any way that this will have any subtasks.
_jensen added a subscriber: _jensen.Nov 21 2018, 7:40 PM2018-11-21 19:40:21 (UTC+0)
abian awarded a token.Nov 22 2018, 2:41 PM2018-11-22 14:41:39 (UTC+0)
abian added a subscriber: abian.Nov 22 2018, 3:52 PM2018-11-22 15:52:21 (UTC+0)
dbarratt added a subscriber: dbarratt.Jan 17 2019, 7:41 PM2019-01-17 19:41:38 (UTC+0)
• Pablo-WMDE added a subscriber: • Pablo-WMDE.May 23 2019, 8:27 AM2019-05-23 08:27:43 (UTC+0)
WMDE-leszek added a subscriber: WMDE-leszek.May 23 2019, 9:11 AM2019-05-23 09:11:14 (UTC+0)
dbarratt added a project: Wikidata-Campsite.Jun 24 2019, 10:38 PM2019-06-24 22:38:39 (UTC+0)
What is preventing this from being enabled on production?
Addshore moved this task from Incoming to Needs Work on the Wikidata-Campsite board.Jun 24 2019, 10:42 PM2019-06-24 22:42:55 (UTC+0)
Addshore added a subscriber: Addshore.
I guess that is up to @Lydia_Pintscher :)
Addshore changed the task status from Open to Stalled.Jun 24 2019, 10:43 PM2019-06-24 22:43:04 (UTC+0)
Lydia_Pintscher added a comment.Jun 27 2019, 2:44 PM2019-06-27 14:44:27 (UTC+0)
From my side we're ready to go to production as soon as @Tpt says his concerns further up have been addressed. @Tpt Care to chime in?
Tpt added a comment.Jun 27 2019, 6:21 PM2019-06-27 18:21:41 (UTC+0)
@Lydia_Pintscher Improving JSON-LD structure like I proposed requires a strong refactoring of Purtle. I would not commit to do it anytime soon and I believe no one else would. So, I don't think it's a good idea to block JSON-LD deployment for that.
Lydia_Pintscher changed the task status from Stalled to Open.Jun 27 2019, 6:33 PM2019-06-27 18:33:46 (UTC+0)
Ok cool! Then let's go ahead.
daniel added a comment.Jun 27 2019, 8:02 PM2019-06-27 20:02:46 (UTC+0)
In T207168#5290088, @Tpt wrote:
If you look into refactoring Purtle at some point, note that many of its quirks are there because it has been hand optimized for speed and memory usage.
WMDE-leszek added a comment.Edited · Jun 28 2019, 6:57 AM2019-06-28 06:57:26 (UTC+0)
In T207168#5290126, @Lydia_Pintscher wrote:
For the sake of the development good practices I'd insist that the same functionality is brought back to beta instance prior to enabling it on wikidata.org. That is that T226472 is fixed first.
Lydia_Pintscher added a comment.Jun 28 2019, 8:32 AM2019-06-28 08:32:42 (UTC+0)
Yeah sounds good.
Lydia_Pintscher closed subtask T226472: JSONLD not recognized as a valid output format for entity data on Beta Wikidata as Resolved.Jul 1 2019, 11:01 AM2019-07-01 11:01:03 (UTC+0)
Christopher added a comment.Jul 2 2019, 8:59 AM2019-07-02 08:59:08 (UTC+0)
Do you foresee any changes to the context/vocabulary/ontology in the future (e.g. implementing processing features of JSON-LD 1.1)? How will context changes be versioned / published?
Could not also the ontology be dereferenceable as a json-ld context? Then you could use @vocab to provide a default for the wikibase properties and types. (e.g. "@vocab": "http://wikiba.se/ontology-1.0.jsonld")
Lea_Lacroix_WMDE added a comment.Edited · Jul 4 2019, 11:33 AM2019-07-04 11:33:34 (UTC+0)
This should be deployed on July 8th. I'll take care of the announcement.
dbarratt awarded a token.Jul 5 2019, 1:46 PM2019-07-05 13:46:51 (UTC+0)
Ladsgroup edited projects, added Wikidata-Campsite (Wikidata-Campsite-Iteration-∞ (On Hold)); removed Wikidata-Campsite, MW-1.32-notes (WMF-deploy-2018-10-16 (1.32.0-wmf.26)).Jul 8 2019, 7:25 AM2019-07-08 07:25:14 (UTC+0)
gerritbot added a comment.Jul 8 2019, 7:29 AM2019-07-08 07:29:30 (UTC+0)
Change 521221 had a related patch set uploaded (by Ladsgroup; owner: Ladsgroup):
[operations/mediawiki-config@master] Enable jsonld output format for wikibase entities everywhere
gerritbot added a project: Patch-For-Review.Jul 8 2019, 7:29 AM2019-07-08 07:29:32 (UTC+0)
Ladsgroup moved this task from To Do (prioritised from top to bottom) to Peer Review on the Wikidata-Campsite (Wikidata-Campsite-Iteration-∞ (On Hold)) board.Jul 8 2019, 7:50 AM2019-07-08 07:50:43 (UTC+0)
gerritbot added a comment.Jul 8 2019, 11:13 AM2019-07-08 11:13:53 (UTC+0)
Stashbot added a comment.Jul 8 2019, 11:16 AM2019-07-08 11:16:54 (UTC+0)
Mentioned in SAL (#wikimedia-operations) [2019-07-08T11:16:53Z] <ladsgroup@deploy1001> Synchronized wmf-config/InitialiseSettings.php: [[gerrit:521221|Enable jsonld output format for wikibase entities everywhere (T207168)]] (duration: 00m 49s)
Ladsgroup moved this task from Peer Review to Test (Verification) on the Wikidata-Campsite (Wikidata-Campsite-Iteration-∞ (On Hold)) board.Jul 8 2019, 11:22 AM2019-07-08 11:22:28 (UTC+0)
daniel removed a subscriber: daniel.Jul 8 2019, 2:15 PM2019-07-08 14:15:37 (UTC+0)
Thadguidry added a subscriber: Thadguidry.Jul 8 2019, 2:34 PM2019-07-08 14:34:08 (UTC+0)
Something is amiss with these...not found.
"wikibase": "http://wikiba.se/ontology#",
"statements": {
"@id": "wikibase:statements"
"identifiers": {
"@id": "wikibase:identifiers"
"sitelinks": {
"@id": "wikibase:sitelinks"
dbarratt added a comment.Jul 8 2019, 2:41 PM2019-07-08 14:41:52 (UTC+0)
In T207168#5313628, @Thadguidry wrote:
Those are URIs not URLs, they don't need to be found... or am I missing something?
Thadguidry added a comment.Jul 8 2019, 4:32 PM2019-07-08 16:32:52 (UTC+0)
@dbarratt in the Wikibase ontology I could not find those properties in the OWL document returned. Sorry, I'm getting caught up with your schema layouts as fast as I can :-) I expected my parser to retrieve information about their description, range, domain. I do see the class "Statement" however.
abian added a subtask: T227712: Set .json-ld as an alias of .jsonld.Jul 10 2019, 9:40 PM2019-07-10 21:40:44 (UTC+0)
Lydia_Pintscher closed this task as Resolved.Jul 15 2019, 10:58 AM2019-07-15 10:58:48 (UTC+0)
Lydia_Pintscher moved this task from Test (Verification) to Done on the Wikidata-Campsite (Wikidata-Campsite-Iteration-∞ (On Hold)) board.


Kevin J. Cope (b. 1981) started as a youthful heavy metal guitarist inspired by the likes of Megadeth and Metallica. His tastes grew into the more intellectual metal of bands like Symphony X and Dream Theater as well as the more psychedelic progressive rock of YES. Through exposure to the classical greats of the 20th century such as Igor Stravinsky, Alberto Ginastera, Richard Danielpour, and John Corigliano his own composition style became more complex while still maintaining a connection to the rock and metal music that he still loves to this day. His unique style incorporates everything from serial techniques, free atonal, Latin dance, Arabian scales, and metal.
A graduate of the University of Delaware with master's degrees in music composition and guitar performance, Kevin has studied with composers Patrick Long, Jennifer Barker, and Andrew Bleckner as well as guitarists Matthew Slotkin and Christiaan Taggart. Kevin has been the recipient of the Emerging and Established Professional Awards for Music Composition as well as the Emerging Professional Award for Music: Solo Recital from the Delaware Division of the Arts and was featured on the cover of Soundboard Magazine in their 40th Anniversary Edition with a focus on composition for the guitar.
A primary composition focus is the classical guitar but Kevin has written for a wide range of instrumentations and has received commissions for many ensembles including horn octet, guitar ensemble (Salisbury Guitar Festival), choir, woodwind quintet, chamber ensembles (Melomanie), and concert band. Solo and duo commissions have also been completed including works for guitar (including the Philadelphia Classical Guitar Society competition), flute and guitar, tenor and piano, saxophone and guitar (Duo Montagnard), and guitar duo (Duo Cortado).
Kevin's first CD Destination Unknown, Original Compositions I, Solo Guitar Vol. 1 was described by Classical Guitar Magazine as "a fascinating and stimulating debut that gets better and better the more you listen to it" while analyzing Kevin's compositions as "though there are certainly nods to less conventional modern strains, and a certain dark solemnity...infuses much of this, it still ultimately feels very accessible."
An experienced guitar teacher, Kevin teaches with the University of Delaware Community Music School and the Darlington Arts Center (Shavertown, PA) as well as teaching private guitar and composition students. Kevin is also an experienced Suzuki Guitar instructor in the Newark, DE area.
Currently the president of the Philadelphia Classical Guitar Society, Kevin continues to work to enhance the influence the classical guitar and contemporary art music in the Philadelphia area.
Kevin's works are available for purchase from StringsbyMail.com, Sheet Music Plus, and this site. Recording releases include Destination Unknown (Solo Guitar Vol. 1), the saxophone and guitar piece Origin Unknown: A Pole Was Journeying, recorded and released by Duo Montagnard and the solo clarinet piece Sirocco recorded and released by Christopher Nichols and Navona Records.


text:
alexandra@yinyangnaturals.com
ALEXANDRA HAAN
In 2007 Alexandra got her start in the natural foods community in Missoula, Montana while working at the Good Food Store and finishing up her degree from the University of Montana's School of Journalism. She instantly saw the connection the store had not only with its immediate surroundings, but also the state at large, bringing a wide variety of products sourced near and far to a very overlooked part of the country.
What began as a curiosity for where food comes from quickly became a passion, ultimately leading her to Whole Foods Market for three and half years. Her Whole Foods career started in Seattle, where she bought for all grocery sub teams, and then to San Francisco as Grocery Buyer. Alexandra continues to explore every topic of the natural food world, including ingredient sourcing, food access, agricultural practices, nutrition, and sustainability.
When she isn't working, Alexandra is almost certainly on a mountain top but might also be found drinking locally roasted coffee or traveling with her partner. She's lived in every corner of the country and a large portion of Europe, but is elated to now call Washington home.
chloe@yinyangnaturals.com
CHLOE DOORES
Chloe got her start in Natural Foods at the age of 21 stocking shelves at Good Earth Natural Foods and has been hooked ever since. She has worked in every capacity from retailer to buyer, broker to merchandiser and beyond. Having grown up in Fairfax, CA she has always been surrounded by and drawn to all things natural, from the food she loves to cook to the trails she loves to hike.
Chloe strives to do mission-based work, and is proud to represent what Yin Yang Naturals and the brands they work with stand for. Along with her passion for food and hiking, she also loves reading, playing guitar, swimming and listening to true crime podcasts.
courtney@yinyangnaturals.com
COURTNEY DAVIS
Courtney grew up in a military family but spent the majority of her upbringing in Harrisburg, Pennsylvania. The mountains called her out west in 2017, and she has been living in Tacoma, Washington ever since. She is new to the natural foods industry, having worked mostly in the nonprofit education sphere for the last 7 years. Most of her previous experience has been in administrative support and she is excited to bring that same support to the Yin Yang Naturals Team! She believes that it is important to know how your food is made and is excited to join a team that supports those who are responsibly caring for our food and our planet.
In her free time, she enjoys bouldering, hiking, exploring new places, and spending time with her 9 nieces and nephews. She is also an amateur photographer and loves to view the world through her Nikon 35 mm prime lens.
danielle@yinyangnaturals.com
DANIELLE HOLLAND
Danielle transitioned from a background in the arts to the natural food industry as her love of organic food, farming, and culture began to steadily increase over the years. She put her production and storytelling skills to use as a natural health event manager, demo coordinator, and brand ambassador to promote various organic brands throughout the greater Seattle area. Her focus in all her work is on communication, compassion, and community building.
She spends every free moment running after her son or flying around on people's feet and hands (acroyoga!)
debbie@yinyangnaturals.com
DEBBIE EASON
Debbie has strong roots in the Pacific Northwest, living in the Portland area her entire life. She has loved being involved in the natural foods industry for over twenty years and enjoys the products and the people!
In her free time, Debbie enjoys spending time with her family, friends, and four-legged critters. She also enjoys traveling, hiking, and an occasional glass of pinot noir.
Key Account Coordinator
emily@yinyangnaturals.com
EMILY HERRICK
Emily credits her maternal grandfather for her lifelong love of organic, natural and 'just really good' food and cooking. She has strong memories of him taking her sisters and her to the local farmers' market and teaching them to find the freshest food. Emily and her husband, Michael founded and ran a farmers' market in Bridgehampton, NY, where they were also Slow Food leaders.
Emily's varied experience includes a stint as a professional organizer helping people clear clutter from their home, work as an advertising proofreader and running the children's department of a public library. She's also a yoga teacher and wedding officiant.
She and her husband made a huge leap of faith in 2014 when they discovered and moved to Vashon Island, WA from the East End of Long Island (based on reading a book written by a dairy farmer from Vashon) and had a home built.
She loves great food, interior design, nature, travel, dogs and spending time with family and friends.
gena@yinyangnaturals.com
GENA NASSET
Gena Nasset brings heaps of practical work experience to the Yin Yang team. Much of her professional history is composed of small business administration and management roles with an emphasis on start-up, marketing and brand development. With exposure to several industries, Gena found a personal passion for the Wellness Industry, and in 2004 began formal training as a therapeutic yoga instructor and Thai massage therapist. Upon completion of that training, in 2007 Gena's entrepreneurial spirit led her to open a community wellness and education center with a focus on providing health services and wellness education to growing families.
In 2012 Gena stepped back from her role as small business owner to focus on her most challenging and fulfilling endeavor yet, motherhood. Great instincts and highly adaptive problem-solving nature lead Gena's enthusiasm to serve.
Native to Missoula, MT, Gena enjoys all outdoor adventures found in Big Sky Country. Camping, hiking, mountain biking, river rafting, hunting and fishing are activities she pursues in her free time. Gena enjoys all aspects of building, remodeling, interior and graphic design and creative writing. Above all, Gena enjoys spending time with her husband and their two young children.
Western Regional Manager- Wellness
ginarenee@yinyangnaturals.com
GINA RENEE DARROW
A West Coast native, Gina Renee hails from a family with deep roots in small scale agriculture of fruit and nut trees, beekeeping, and practicing preservation of the garden harvest. The granddaughter of a farmer and an arborist, she was graciously raised with respect for our earth and the recognition of where food comes from - from farm to table and honor for the people that are a part of that process.
Gina Renee has worked in the natural foods industry since 1998 and has seen the market grow and evolve. She has a strong understanding of the business as both a buyer and vendor and absolutely loves working with the folks in this industry.
She spends her free time exploring, making food, getting dirty in the garden, throwing clay, hanging out with her four legged critters, and laughing with her son, husband, family, and friends.
lynda@yinyangnaturals.com
LYNDA PATTON
Lynda has been a Pacific Northwest resident since 1963, moving to Portland Oregon in 1979.
She started her career in retail sales in 1989 calling on small format stores and eventually moved over to larger chains in 2007. At Advantage Sales and Marketing, Lynda serviced the Natural Food channel, including Whole Foods Market, New Seasons Market and independently owned stores and co-ops.
In 2013, she started calling primarily on New Seasons Market, becoming part of the sales merchandising reset team.
Lynda enjoys reading and spending time with her three grandsons
Senior Territory Manager
jamie@yinyangnaturals.com
JAMIE STUCKENSCHNEIDER
Jamie was born and raised in Portland Oregon. She loves calling the Northwest her home. She started climbing the corporate ladder at Target in 2003, eventually promoted to Market Department Team Lead, where she gained her experience with vendors, ordering, and food.She got her start in the Natural Foods industry in 2008 at Food Front Coop as the Dairy/Perishable Grocery/Frozen buyer until 2014. Here, she excelled her natural foods knowledge and built strong relationships with local/regional/national food lines.
She loves helping get small businesses off the ground. She also loves visiting the production kitchen, slapping on an apron and helping her vendors produce!
She and her partner love to hike, attend Orangetheory classes, watch movies with their cats, and travel the world!
paul@yinyangnaturals.com
PAUL STOGNER
Soon after his move from Flint, Michigan to Portland, Oregon, Paul began his career in natural foods with Nature's Fresh Northwest/Wild Oats in 1996. He quickly worked his way up to grocery manager and held this position until October 2000 when he accepted an opportunity with Mountain Peoples/ UNFI.
During his time with UNFI Paul worked with a wide variety of accounts including Wholefoods, NCGA, INFRA, supermarket and hundreds of independent retailers and vendors. He also developed his skills in sales, the development of Field Day and Blue Marble Brands, category management, store setting and layout.
In 2014 Paul moved to Yin Yang Naturals where he works with New Seasons Market, Market of Choice and numerous independent retailers continuing to increase his skills in sales and merchandising. He has also been a member of the Provender Alliance since 2000.
Paul enjoys travel, the outdoors, organic gardening, good food and music.
sam@yinyangnaturals.com
SAM NASSET
Born in Oregon and raised in Montana, Sam spent 11 years at Good Food Store, Montana's largest independent natural foods retailer, working in several departments along the way, and finally landing as chill/frozen buyer. He jumped on the Yin Yang bus in the spring of 2014. His experiences as a buyer, hard work ethic, and attention to detail have earned him respect among his colleagues in the industry. He covers the wide open territory of Montana, Eastern Washington, and Northern/Eastern Idaho.
Sam enjoys hunting for food, playing guitar, gardening, cycling, and spending time with his wife Gena and two kids.
victoriadesantis@yinyangnaturals.com
VICTORIA DESANTIS
Victoria is a California native who currently lives in Altadena, CA with her husband, two teenage children and their dog Kiki. She grew up in Bakersfield and moved to Santa Barbara to attend college.
Victoria was living in Los Angeles for a time while working in the entertainment industry and fell into the natural foods world by accident. After eating a Balance Bar and noticing on the wrapper that their headquarters were based in Santa Barbara, she reached out with her resume and Balance Bar offered her a position and she moved back to Santa Barbara. This led to a 20+ year journey in the natural foods industry. Shortly after her first baby was born, she reconnected with some former Balance Bar colleagues and joined the Sales & Marketing team at Amy's Kitchen where she stayed for 15 years.
© 2019 Yin Yang Naturals


This weekend, you can stay cool at Festival du Voyageur, see Romeo and Juliet as a ballet or in a kid-friendly theatre production, and hear a Grammy-nominated spoken word artist drop the mic at Queer People of Colour Winnipeg's latest event.
Three of CBC Manitoba's on-air personalities share their top picks for the Feb. 15-17 weekend.
It's the event you winter-loving Manitobans have been waiting for.
Festival du Voyageur celebrates its 50th anniversary this year.
The opening ceremony kicks things off at 7:30 p.m. on Friday, with the festival's ambassadors recreating the May Ceremony — a voyageur tradition to honour those who can't be there, and to remember friends and loved ones from along the journey.
And of course — there will be fireworks.
You can also check out musical acts like Amadians, 3Peat, Kakagi and Dominique Dupuis, an internationally acclaimed francophone violinist from New Brunswick.
You'd really be missing out, though, if you missed Ensemble folklorique de la Rivière-Rouge. The local traditional jig and musical dance troupe performs annually at Festival. But this year is special for member Véronique Demers.
Demers has never missed a Festival in her 24 years. In fact, it's where she found her love for violin when she was watching performers as a child. Now studying to be a doctor in Ottawa, she's made the trip back to Winnipeg for the 50th anniversary.
Between shows, you can grab a drink at Festi-Bar on Ice, the frozen bar on the Assiniboine River. And yes, as always, you can also pour syrup onto snow and eat cold sugar off of a stick.
Single daily tickets range from $5 to $25, depending on your age. You can get those online.
If you are still feeling in the mood for romance (or a good cry) post-Valentine's Day, a classic tale of love and loss may be just the ticket.
Shakespeare's star-crossed lovers are taking to the stage this weekend in two separate productions.
For those yearning for a classic, the Royal Winnipeg Ballet will transport you to 16th-century Verona, Italy for Romeo and Juliet, a ballet based on Shakespeare's famous play, with Sergei Prokofiev's music performed by the Winnipeg Symphony Orchestra.
The RWB's 125-minute production is on at the Centennial Concert Hall Thursday through Saturday at 7:30 p.m., with the final show 2 p.m. on Sunday. Tickets are available online or by calling the RWB box office.
For those looking a twist on the iconic tale, Manitoba Theatre for Young People is presenting What if Romeo & Juliet…, a show from Montreal's Dynamo Théâtre.
This version tells the story of the feud between the Capulets and Montagues through acrobatic dance and asks the question — does Juliet and Romeo's story have to end in tragedy?
The show runs 60 minutes and is suitable for ages nine and up.
What if Romeo & Juliet… opens Friday night and runs until Feb. 24 at at MTYP at The Forks.
You can catch the show this weekend at 7 p.m. Friday, 4 p.m. on Saturday, or Sunday at 1 p.m. and 4 p.m.
Next weekend, there's a French-language performance on Feb. 23 at 4 p.m., and English-language performances Feb. 24 at 1 p.m. and 4 p.m.
Tickets are available through the MTYP website or at the theatre.
Friday night, Queer People of Colour Winnipeg will be hosting the ninth edition of QTPOC Drop the Mic.
The headliner of the evening will be Atlanta-based, Grammy-nominated spoken word artist Queen Sheba. Sheba's poetry chops have been flexed and recognized in competitions around the world, including the National Poetry Slam in the U.S.
The event's organizers encourage queer, trans, and two-spirited self-identified black people to sign-up to share their performance art in a five-minute window. Artists interested in performing can slide into QPOC's DM's at @qpocwinnipeg on Instagram, or e-mail qpoc204@gmail.com.
QTPOC Drop the Mic #9 is an all-ages event happening at the X-Cues Cafe & Lounge, located at 551 Sargent Ave. in Winnipeg, on Friday, Feb. 15, from 6 to 9 p.m.
Cover is $10 and tickets are available online, but organizers say no one will be turned away due to lack of funds.
Starting at 10 p.m. in the same venue, the open mic will be followed by the 18+ event, the QTPOC Black History Month Dance Party, featuring DJ Jordan and Petty Wap.