dataesr/scientific-paragraphs-categorization huggingface dataset:
At the same time, the detail with which particular aspects 25758314, 0, Downloaded from https://besjournals. onlinelibrary. wiley. com/doi/10. 1002/pan3. 10466 by Bibliothèque De L'Epfl-, Wiley Online Library on [20/03/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License of biodiversity can be identified using deep learning, including specific species of flora and fauna, means that better connections can be made between biodiversity and CES  (Echeverri et al. , 2020). For example,  Cardoso et al. (2022)  categorised CES related to biodiversity by identifying images of species on social media using deep learning and a training dataset labelled by the authors themselves.
We focus on Great Britain as our case study area and use iNaturalist data to compare activity between social media and citizen science. In doing so, we seek to answer the following research questions: (1) How can deep learning and crowdsourced data capture a broad range of human-species interactions on Flickr? (2) What is the distribution of human-species interactions using Flickr data as a measure of CES related to biodiversity? (3) What species and species groups are users interacting with? (4) How do users' species interactions compare with biodiversity metrics as an indicator of wider cultural value? Answering these research questions will help create a better understanding of the types of preferences being expressed on social media in relation to biophysical features, an important aim of CES assessment.
| Study area data To apply our deep learning approach at national scale in Great Britain, we used a Flickr image dataset created in previous research which provided 9. 8 million geo-located images depicting outdoor scenes  (Havinga et al. , 2021a). These images were downloaded using metadata records retrieved through the Flickr Application Programming Interface (API) and filtered to outdoor images using the Places365 deep learning model  (Zhou et al. , 2017). At the same time, for comparison, we compiled a new dataset of research-grade iNaturalist observation metadata, including image urls, by using the rinat package in R to access the iNaturalist API. In total, we downloaded 1. 1 million iNaturalist records for Great Britain. The image corresponding to each record was accessed using the image urls during model prediction.
| Training and test data To identify human-species interactions in images, we compiled a large dataset of images from iNaturalist and Flickr to train our deep learning-based species interaction model. For training, we used images uploaded to iNaturalist as a representation of people's interactions with individual species and Flickr images as a representation of all other, 'generic' types of interactions to train our model. To download the images, we used the 'flickrapi' library in Python to access the Flickr API and the rinat package in R to access the iNaturalist API. We downloaded images using a randomly generated sample grid, representing a 10% random sample of a 25 km resolution grid over the whole of Europe (Supporting Information Figure  S1 ). Within each grid cell, we downloaded an equal number of images from each source by randomly downsampling the greater image set in the case of imbalances.
| Model architecture Our model consisted of a ResNet-18 with an additional five layers replacing the two final layers  (He et al. , 2016). The model was developed using the 'PyTorch' library in Python. Using PyTorch's built-in model library, we downloaded the original architecture with the model weights pretrained on the ImageNet database. We then adapted the model to produce a binary output, each converted to a 0-1 range using a softmax transformation. This output represented either a human-species interaction, such as those observed on iNaturalist, or a generic interaction, consisting of any other kind of interaction. Generic interactions were only observable in the Flickr data and included, for example, images of buildings, sporting events, transport and landscapes.
| Model training The first step was to train the model to distinguish between images depicting human-species interactions and generic interactions. All iNaturalist images were labelled as human-species interactions and all Flickr images were labelled as generic interactions. We set our deep learning model to train on the image dataset for 10 epochs with an initial learning rate of 1e 05 for the weights of the final two layers and 1e 04 for the rest of the network. The learning rate was halved every epoch. If the model failed to converge, we also tried halving the initial learning rates. We used Adam as our optimisation algorithm and a cross-entropy learning loss  (Kingma & Ba, 2014). Therefore the standard, cross-entropy loss for a single image during model training was calculated as: with  denoting the loss calculated over y, the training labels, and ŷ, the model predictions, for C number of classes (two), with a softmax applied to the model class predictions before calculating the loss.
| Selecting the dilution coefficient To help identify the most optimal β dilution coefficient, we calculated the entropy across the species classification scores predicted by the species classification model (introduced in Section 2. 4) in Flickr and iNaturalist observation images in Great Britain. A much larger entropy across Flickr images predicted as human-species interactions as compared to generic image predictions and iNaturalist images would indicate a significant drop in model confidence and suggest the inclusion of irrelevant images as human-species interactions. We calculated entropy as: with H denoting the entropy across the 8142 individual species classification scores ŷ. As an additional accuracy measure, we also conducted a visual check of the images predicted as human-species interactions in the test dataset at 0.
42 reflecting the mean entropy of image classification scores in the dataset (Supporting Information Figure  S2 ). At species class level this filter was not needed because accuracy at phylum level was already found to be high at 89%. However, at genus and family level, accuracy needed to be improved to support the reliability of the results. The bird species abundance maps were generated in previous work using generalised additive models (GAMs;  Massimino et al. , 2015).
| Species interaction model The accuracy of the species interaction models trained using different β coefficients is shown in  This shows how the dilution coefficient enables the model to detect a wider range of human-species interactions. However, there was a much larger drop in accuracy in detecting generic interactions relative to this accuracy improvement and versus the accuracy of the baseline model. We therefore found the β = 0. 01 model to be the most optimal in terms of its accuracy on the test dataset, maximising its ability to detect human-species interactions while maintaining a high level of overall accuracy. For each β model's image predictions of human-species interactions, we also calculated the mean entropy across the species class scores predicted by the species classification model.
We found entropy to increase from 2. 85 for the baseline model to 3. 87 for the β = 0. 001 model. This amounted to a 36% increase. For the β = 0. 1, a much smaller increase of 1% to 2. 89 was observed, with a small increase of 7% for the β = 0. 01 model. The entropy recorded against the models' generic image predictions stayed relatively similar, only increasing from 5. 20 for the baseline to 5. 42 for the β = 0. 001 model. In comparison, the entropy recorded for the species model predictions on the iNaturalist dataset for Great Britain was 2. 42. A full set of randomly sampled images of each β models' predictions at different confidence levels can be found in Supporting information Table  S1  online. Overall, we observed a good ability by the β = 0. 01 model to identify species images, even at low confidence levels, with a very small amount of generic-type images present among the models' predictions and the most and least confident predictions staying consistent.
| Predicting cultural preferences for biodiversity using deep learning Previous research has categorised images on social media related to human-species interactions using deep learning  (Cardoso et al. , 2022; Edwards et al. , 2021; Lee et al. , 2022). However, our study represents one of the first studies to do this at scale, using crowdsourced data and with spatial metrics linked to specific species, a key research frontier in the field of CES research  (Gould, Bremer, et al. , 2020) , especially in relation to biodiversity  (Echeverri et al. , 2020). Drawing on such a large dataset of images from iNaturalist meant the interactions of a wide range of people could be incorporated into the species interaction model. In turn, the inherent noise in the Flickr data could be better accounted for with the implementation of the β dilution coefficient.
This meant the scope of cultural interactions with individual species could be expanded to include a broader representation of interest on Flickr without the need for manual labelling by the authors themselves. The implementation of the β dilution coefficient drew on previous work in the machine learning literature surrounding noisy labels  (Marcos et al. , 2022; Reed et al. , 2015). As a technical solution for deep learning applications, this limits overfitting and enables models to generalise better across new datasets  (Song et al. , 2020). We species image predictions remained such as images of stone carvings or people in natural settings. Nevertheless, the model's most confident predictions remained consistent. To further validate its predictions, the image tags and descriptions could be used which often contain information related to the purpose of the image  (Havinga et al.
Overall, across 8142 species classes, the accuracy of the species model at the genus level was fairly low without a filter and led to an overall accuracy of 32% (Supporting Information Table  S2 ). This improved to 50% by applying the entropy filter. A more conservative entropy filter could further improve accuracy at the expense of reducing the size of the obtained dataset. However, the accuracy of the model varied with the genus (Supplementary Table  S3 ). The majority of the most frequently captured genera were identified with 70% accuracy or more and the observed drop only concerned a limited amount of genera. This can be attributed to the great visual similarities between some species classes, such as in the case of the American deer Odocoileus genus, which was predicted as one of the most frequently captured species in Flickr images.
| Differences in user preferences between social media and citizen science The results of our study reveal key differences between the interactions occurring on social media and citizen science. This was in line with our hypothesis that Flickr and iNaturalist users would express different preferences for individual species and supports calls to include a wider variety of big social datasets in CES assessment  (Ghermandi & Sinclair, 2019; Scowen et al. , 2021). Accurate information on the demographics of social media users is very difficult to obtain, especially at large scales  (Lenormand et al. , 2018). In the absence of such information, a greater number of datasets representing the cultural preferences of a wider range of people can produce a more representative picture of the CES being generated by nature, so-called 'data mashups'  (Ghermandi & Sinclair, 2019).
better understand the types of human-species interactions occurring on social media versus citizen science, we applied a second deep learning model to classify the individual species in Flickr images identified as human-species interactions and compared this with iNaturalist. Using the species classification model's predictions, we examined what the most frequently photographed species were at the genus level on Flickr versus iNaturalist. To make the fairest comparison and control for biases between datasets, we also ran the model on the images of the iNaturalist observations and used its predictions to compare the two datasets. To classify species in images, we applied the pretrained 2018 iNaturalist competition winner model, which is capable of detecting 8142 species. The model consists of a fine-tuned Inception V3 deep learning model, pretrained on ImageNet.
coefficient. The overall accuracy of the baseline (β = 1) model was high, at 97. 8%. The β = 0. 1 model achieved a similarly high 97. 6% accuracy, followed by the β = 0. 01 model with a 95. 6% accuracy and the β = 0. 001 model, with an accuracy of 90%. Increasing the value of the β coefficient by another factor of 10 to β = 0. 0001 failed to produce a working model. The drop in accuracy in detecting generic interactions in Flickr images can be directly related to the human-species interactions that are also found within the Flickr dataset. This was also reflected in the ability of the β = 0. 001 model to predict images from iNaturalist as human-species interactions with almost perfect accuracy.
have used this technique to capture a broader set of interactions, and therefore preferences, between the two datasets, with accuracy improvements reflecting back on the percentage of species images captured by the model in the iNaturalist dataset. Further visual inspection confirmed the predictions as reliable. However, this also revealed that the β model approach did not solve the problem completely, as a small number of more generic images in the model's F I G U R E 7 Total monthly interactions on Flickr and iNaturalist with migratory bird species of conservation concern.
To gain a better understanding of the cultural preferences being expressed on social media, we compared human-species interactions on Flickr with ecological measures of biodiversity as an indicator of wider cultural value. This wider value includes the intrinsic and relational values of nature associated with stewardship and the living existence of species irrespective of any in situ interactions with people (Anderson et al. , 2022). To do this, we compared perceived bird species richness based on the number of species in Flickr images with a measure gen- F I G U R E 2 The effect of the β dilution coefficient on the species interaction model's decision boundary. By placing a less strict emphasis on the training labels, applying the β coefficient allows more images within the Flickr dataset to be identified as human-species interactions.
In the plane-parallel approximation at any angle θ 9  , ( ) ( ) ( ) ( ( )) ( ) q s q q q = = h T T T T , , 1 9 s S B s 4 s s b where ( ) T T s b is the T s -T b relation. For the latter, we employ the envelope model of  Potekhin et al. (1997)  with the parameter η set to 10 -18. This is equivalent to an iron (non-accreted) heatblanketing envelope. Combining Equations (  18 ) and (19) one obtains the surface boundary condition. We have developed a code based on this formalism and called it NSCool 2D Rot to emphasize inheritance from NSCool, a publicly available NSs thermal evolution code in 1D  (Page 2016). For more details, see Appendix B.
g. ,  Hoffman & Frankel 2001)  or the alternating-directions implicit method (see, e. g. ,  Peaceman & Rachford 1955)  were either not converging or converging very slowly during our initial tests. Before using NSCool 2D Rot for practical applications, we performed extensive tests of the solver itself, checked the overall robustness of the spatial and temporal discretization, and compared the results with NSCool for spherically symmetric configurations. To check the solver we exported the function that generates a set of discretized nonlinear algebraic equations at each time step to Python using f90wrap tool. Then we solved exported equations employing scipy. optimize. root at exactly the same time steps as in NSCool 2D Rot and compared the resulting 2D temperature distributions. We set the tolerances (maximum   dT T , where  dT is a Newton-Raphson correction to  T ) identically in both solvers (i.
e. , in Python and NSCool 2D Rot) and equal to 10 -10. The maximum relative difference in  T between two solvers solving exactly the same equations did not exceed 3 × 10 -10 for all time steps before the age of ∼50 yr (further in time Python solver was not converging). Then, we compared cooling curves computed by NSCool 2D Rot with different spatial grids and time steps. Doubling the number of grid points in both the r and θ directions (i. e. , 4 times more grid points in total) resulted in a maximum relative change in  T s,e of ∼0. 8%. This is on a par with the behavior of NSCool where doubling the number of grid points leads to an ∼0. 7% maximum relative change in surface temperature. Doubling the number of time steps resulted in an ∼1. 6% maximum relative change in  T s,e. Again, this is on a par with NSCool and was expected as the requested temperature change between consecutive time steps 13  was 5% and 2.
The authors thank D. Yakovlev for useful discussions. M. B. and A. R. acknowledge support from a grant from the Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI, Project No. PN-III-P4-ID-PCE-2020-0293, within PNCDI III. D. P. and M. B. acknowledge financial support by the Mexican Consejo Nacional de Ciencia y Tecnología with a CB-2014-1 grant No. 240512 and the Universidad Nacional Autónoma de México through a UNAM-PAPIIT grant No. IN109520. M. B. also acknowledges support from a postdoctoral fellowship from UNAM-DGAPA. J. N. gratefully acknowledges the Italian Istituto Nazionale di Fisica Nucleare 9. The ratio of polar to equatorial redshifted temperature as a function of time at different densities inside the NS. The considered EOS model is APR.
After 2 weeks, one reminder was sent to the dermatologists who had not yet responded. According to recommendations of the CROSS-Checklist all answers were checked for clarity and inconsistencies and if needed clarified by the concerned dermatologist. Single stage sampling was used since all the data were extracted from the questionnaire during the response period of 8 days. As there are no data available about the incidence of laser-induced leukoderma or vitiligo, an upfront sample size calculation was not possible. The occurrence of laser/ IPL-induced leukoderma or vitiligo was calculated by all the potential laser/IPL-induced leukoderma or vitiligo patients that were reported, divided by all the vitiligo patients they approximately had encountered (face to face) over the past year to calculate the sample size.
The data set was extracted from Google Forms into an Excel format and checked for irregularities by NP, and NB. Then, the Excel sheet was imported into IBM SPSS statistics 26 to provide descriptive statistics. No other statistical tests have been performed. Ethics approval for this study was not necessary, as patients were not directly involved and the collected patientrelated data were completely anonymous. The link to the questionnaire was sent directly to the vitiligo experts after approval was given to partake in the study. Furthermore, the questionnaire could only be filled in if accompanied by an e-mail address which was checked to prevent multiple participation.
The question of how behavioural diversity in humans and other animals is shaped by the combined in uence of demography, genetics, culture, and the environment receives much research attention. We take a macro-ecological approach to evaluate how chimpanzee (Pan troglodytes) behavioural diversity is spatially structured and associated with genetic diversity (i. e. heterozygosity as a proxy for effective population size) and contemporary and historic environmental context. We integrate the largest available chimpanzee behavioural and genomic datasets and apply spatially explicit Bayesian Generalised Linear Mixed Models to derive marginal effects for putative drivers and range wide spatial predictions of probability to observe behavioural traits. Contrary to expectations from neutral models of behavioural evolution, we observed a negative association of genetic diversity with behavioural diversity.
In this study, we advance previous work through the simultaneous analysis of effects for genetic, ecological, and spatial predictors on chimpanzee BD. We integrate the largest behavioural and genomic datasets for chimpanzees with ecological context, by incorporating behavioural datasets from the Pan African Programme: The Cultured Chimpanzee (PanAf)  (Kalan et al. 2020 ) with newly available genetic, genomic  (Lester et al. 2021 , Fontsere et al. 2022) , and chimpanzee range stability  (Barratt et al. 2021 ) datasets, and with publicly available paleoclimate and ecosystem productivity (hereafter net primary productivity) data. Our models produce range-wide predictive maps of the probability to observe unique behavioural traits and overall behavioural diversity. We also use model predictions to compute a matrix of 'behavioural dissimilarity' between pairs of communities to enable comparisons with geographic and genetic distance.
2019) and the distance from Pleistocene refugia  (Kalan et al. 2020). Our nal dataset comprised information on 25 behaviours related to feeding, communication, and thermoregulation for 144 communities distributed across the chimpanzee geographic range (see Methods for details). We tted separate models in which we used, as a measure of genetic diversity (proxy for historical effective population size), heterozygosity derived from whole chromosome 21 sequence (chr21) and microsatellite (msat) data. Measures of heterozygosity were spatially interpolated to match the number of communities where behavioural data was sampled (see Methods section for details). Our models effectively predicted the observed data on chimpanzee BD (i. e. , the number of behavioural traits actually observed in each community).
Discussion By integrating the largest available genomic and behavioural datasets for chimpanzees with high resolution environmental spatial layers, we were able to evaluate the relationships between demographic population history (using genetic diversity as a proxy), current and historic environmental predictors and behavioural diversity using a macro-ecological approach. We observed a clear negative correlation between heterozygosity and BD at chromosome 21 (chr21 model) and a similar signal using heterozygosity at microsatellite loci (msat model) (Figs. 2, S2 ). As (neutral) heterozygosity is a measure of genetic effective population size, this nding suggests that, while controlling for environmental predictors, chimpanzee populations with larger historical population sizes have lower BD than populations experiencing stronger genetic drift.
2022). However, the lack of a substantial effect of temperature and precipitation anomalies on BD in our analyses (Figs. 2  and 3 ), suggests that climate anomalies since the Last Glacial Maximum (LGM), measured over a small radius around each community's location (30 km in our case) do not ideally match the spatial and temporal scale for the evolution of chimpanzee BD. We limited the different possible spatial and temporal predictors in our models as not to incur a high rate of false positives. Our models, moreover, did not reveal a substantial effect of distance from refugia on BD (Fig. 2 ). Despite some details of our statistical approach and dataset (see Methods) differing from those of  Kalan et al. (2020) , we are con dent that the lack of a clear effect of distance from refugia in our models does not depend on these differences, as a reduced model including the same main effects tested by  Kalan et al.