dataesr/scientific-paragraphs-categorization huggingface dataset:
The dark green dots are raw counts from the data: number of frogs encountered during annual surveys. These latter values are not corrected for detection, and thus underestimated. They are only provided here for reference increases in population size, all populations have been declining on average over the 19 years of study, except at Little Rock Creek (Table  1 ). But the most striking features in these populations' dynamic are the spatial and temporal variabilities. The across-site standard deviation of population growth rates amounts to 46% (SE = 21%) of their average value, and the across-year standard deviation amounts to 99% (SE = 6%). Finally, from the Little Rock Creek dataset, we estimated the carrying capacity of a suitable site at K = 590 individuals (SE = 15. 4, 95% CI = [559, 620]).
ACKNOWLEDGMENTS We would like to thank  U. S. Fish and Wildlife Service, California Department of Fish and Wildlife , and  USDA Forest Service  for funding this work. We are grateful to  Jesse Bennett ,  Stacey Love  and  Laura Patterson  for their advice on understanding R. muscosa recovery and for providing permits. This is contribution 836 of the  U. S. Geological Survey Amphibian Research and Monitoring Initiative (ARMI). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government. The findings and conclusions in this article are those of the authors and do not necessarily represent the view of the  U. S. Fish and Wildlife Service.
the density of the aqueous fluid at P and T (g cm -3 ). For La, Sm, and Gd, the strong absorption of the signal by the experimental setup (air between detectors and autoclave + Be windows) and sample (4 mm of aqueous solution) at the low L III edge energies (5. 483-7. 243 keV) however prevented collecting XAS spectra in transmission necessary to calculate their aqueous concentrations based on Beer-Lambert's law. Instead, we propose to use the absorption edge jump Eh from fluorescence spectra (calculated with Athena program  83  ) as an indicator of "apparent" solubility trends. For a fixed photon path (i. e. , autoclave and fluorescence detectors are kept at the same position throughout the experiments), the fluorescence signal is correlated to the concentration of the excited atoms, but also the density and the absorption of the different solutions at different P-T conditions.
Speciation analyses. The stability of Gd and Yb hydroxyl-carbonate complexes in 0. 7 m NaCO 3 and 0. 7 m NaCO 3 -0. 35 m NaF solutions was investigated simultaneously to solubility. The XAS spectra were collected from -200 to +800-1000 eV across the corresponding REE L III -edges. (6. 716 and 8. 944 keV). XANES and EXAFS data were analyzed with the HORAE package  83  , using FEFF version 9. After normalization and background removal, the EXAFS oscillations were fitted, using a starting model of Gd and Yb(CO 3 )(OH)  84. The k n -weighted data (n = 1,2,3) used in the fit ranged from 2 to 9 Å -1. The fitting was done in R-space over the range 1. 0 to 4 Å. EXAFS transmission signals were noisy and thus fluorescence data were used for the analyses. Aqueous references of Gd 3+ and Yb 3+ in 0. 1 m HCl were used to determine the amplitude reduction factor S0 2 (1 for both elements).
Acknowledgements This contribution was made possible by regular access to the CRG beamline BM30-B granted by the  ESRF  and SOLEIL synchrotrons. M. Louvel acknowledges funding under the  EU H2020 Marie Curie-Skłodowska Action (Individual Fellowship 797145  "REESources" to M. L. ). The authors also want to thank  A. E. Williams-Jones  and  A. Migdisov  for useful discussions. The DFT calculations in this work were supported by resources provided by the  National Computational Infrastructure (NCI)  supported by The  Australian Government  ( DP190100216  to J. B. ).
Seed size is a key trait for understanding and predicting ecological processes in a plant community. In a tropical forest, trees and lianas are major components driving ecosystem function and biogeochemical processes. However, seed ecological research on both components remains limited, particularly phylogenetic patterns and relationships with other traits. Here, we compiled a unique dataset of seed size (seed mass and geometrical size metrics) based on collections of more than 5,200 seeds of 196 woody plant species, covering >98 and 70% of tree and liana stems, respectively, located on a 30-ha plot in a tropical evergreen forest in central Thailand. We aimed to (1) develop allometric equations among seed size metrics to predict seed mass;  (2)  examine phylogenetic influence on seed size variation; and (3) examine relationships among seed traits and several other functional plant traits.
Our allometric equations relating seed mass, seed volume, and width were well-fitted with data (R 2 = 0. 94, 0. 87 respectively). A phylogenetic signal test found that seed size was randomly distributed across the phylogeny. To study the functional trait relationships, we separately tested seed size data of the tree and liana communities (146 and 50 species, respectively), against mean body size of frugivores, successional niches, leaf, and structural traits. For the tree community, seed size was significantly related to mean body size of frugivores, which we believe is a basic driver of seed size because it is related to the gape width affecting dispersal effectiveness. Nearly all leaf traits were significantly positively correlated with seed size (p < 0. 03). The significant positive correlation of leaf area and greenness suggested the high-energy demand of large-seeded species.
, 2019). Generally, the achievement of seed dispersal is determined by whether a seed can pass into the mouth of a frugivore and be taken away from a fruiting tree. A key disperser trait is gape width, which should be wider than seed width  (Fleming et al. , 1993). Although many physiological and ecological factors may produce selection on seed size, the availability of effective dispersers may place an upper limit on seed size. Strong evidence that gape width of frugivores exerts selective pressure on seed size comes from a study showing a reduction of seed size in Neotropical forests with high defaunation intensity of large-bodied dispersers compared to non-defaunated forests, a phenomenon that has occurred only within the last 100 years  (Galetti et al. , 2013). However, since gape width data are difficult to collect, body size is often used as a proxy.
, 2015). Here, we compiled data on seed size for a majority of species in a tropical forest community, which was challenging because of the irregular fruiting or masting of most tree species  (Suwanvecho et al. , 2017; Kurten et al. , 2018). Lianas are equally important and typically comprise about one-third of woody plant diversity in tropical forests  (Schnitzer, 2018). Studies of lianas have lagged those of trees, and only a few studies have examined seed size or mass in more than a minority of liana species (e. g. ,  Gallagher and Leishman, 2012). Here we collected a seed size dataset of >5,000 seeds measured in five metrics: seed mass, seed volume, seed length, seed width, and seed thickness, covering >98 and > 70% of tree and liana stems, including many liana species not previously reported in TRY global databases (see Supplementary Table  3 ).
Seed Sampling and Measurement Plant voucher specimens, including seeds, are stored in the BBH herbarium at BIOTEC of the National Science and Technology Development Agency, Thailand. Many seeds (38. 3%) have been collected from gibbons' feces during gibbon monitoring. Most seeds of species dispersed by gibbons (through defecation) have hard seed coats and do not change in their morphology during gut passage. However, most seeds (46. 4%) were collected while monitoring plant phenology. Rarely, when seeds were not available in the herbarium, mostly from rare or non-fruiting species (15. 3%), we used information from field-recorded and unpublished data of Mo Singto plot. Seed mass refers to the dry weight of endosperm and embryo including the seed coat but excluding any morphological structures, such as wings, that aid dispersal  (Leishman and Westoby, 1994b).
, Ltd. , Aichi Japan) for the thickness and toughness measurements. Finally, we dried the same leaves at 70°C for 3 days and weighed them to obtain specific leaf area (leaf area per mass unit). To find the area of each individual leaf, we automatized leaf areas of all pictures scanned using the R package "LeafArea"  (Katabuchi, 2015). Overall, we measured leaves of 146 tree species and 46 liana species (Table  1 ).
However, we also assigned "unclassified" for species with inadequate information for the previous classification analysis. Wood density values were derived mainly from the global wood density database  (Chave et al. , 2009). When wood densities were not available at the species level, we used genus level and family level (for 43. 2 and 17. 8% of species, respectively). Maximum tree heights were calculated using the DBH-height allometric equation developed based on the dataset of Southeast Asian tropical forests  (Feldpausch et al. , 2011). We used the BIOMASS R package to calculate tree height  (Réjou-Méchain et al. , 2017).
Seed Size Allometry We estimated seed volume using the geometric formula for a 3-dimensional ellipsoid (Equation  1 ) or a 2-dimensional spheroid (Equation  2 ): V LWT = ( ) p / 6 (1) or V LW = ( ) p 2 6 / ( 2 ) where V represents seed volume; L, W, and T are the length, width, and thickness of a seed (mm). For the most species (N = 188 species), we used the ellipsoid equation, whereas the spheroid equation was used for seeds in which only length and width were measured (eight species). Because seed mass and volume were skewed, we transformed data with natural logarithm prior to analyses. We fitted the seed mass data and all four seed geometric variables (volume, length, width, and thickness) using a linear regression model. We also visualized the distinction between models for trees and lianas.
Phylogenetic Analysis, Trait Coordination, and Trade-Offs We conducted comparative phylogenetic analyses to account for the influence of phylogeny in the allometric analysis. We used the phylogeny compiled by  Smith and Brown (2018) , which is resolved completely at the family level and contains 80% of the genera of seed plants (Spermatophyta) in the world. This phylogeny provided the best overlap with our data, demonstrating the best match at the genus level (98% of the 144 genera). We used Smith and Brown's megaphylogeny at genus level implemented in the software V. PhyloMaker  (Jin and Qian, 2019)  as a backbone to generate a phylogeny. Additionally, we tested for phylogenetic signal in seed size using the function "phylosignal" in the package "picante"  (Kembel et al. , 2010). Phylogenetic generalized least square method (PGLS) was used to build regression models in phylogenetic context with the "gls" function in R-package "nlme" and phylogenetic correlation structures from the R-package "ape"  (Paradis and Schliep, 2019).
We created two candidate models-with and without accounting for phylogeny (PGLS). We performed AIC-based model selection to find the best fitting model of all trait variables (Table  1 ). The predictor variables of tree species included six continuous traits, that is, maximum height, wood specific gravity, leaf area, leaf toughness, specific leaf area, leaf greenness, mean body size of frugivores, and the regeneration niche (a categorical variable). We separated the analysis of lianas because of their contrasting life history and lack of data on height, wood density and regeneration niche (Table  1 ). We tested for correlation (Pearson's coefficient) among all predictor variables (see Supplementary Table  2 ). All statistical analyses were conducted using R 4. 0. 2 (R Core Team, 2016). The ancestral states of seed mass were reconstructed by maximum likelihood using  the function ace in ape under a Brownian motion of continuous trait evolution.
CONCLUSION We created seed size allometry equations for converting between seed mass and seed volume, which is not primarily a consequence of phylogenetic effects. In addition to seed size, we compiled metadata for other functional traits and introduced a new trait, the mean body size of frugivores. The seed trait and plant functional trait relationships revealed both a driver (mean body size of frugivores) and trait coordination between seed size and other leaf traits of tree species, but there were no trade-offs (negative relationships) with other traits. This pattern was not due to any phylogenetic effect. In contrast, the results of the corresponding analysis of lianas revealed the absence of all patterns. These results do not support all findings from other or global studies  (Díaz et al.
ACKNOWLEDGMENTS We are grateful for  Umapawn Matmoon ,  Jantima Santorn ,  Ratasart Somnuek , and all research staffs of the  National Biobank of Thailand  who helped to collect and prepare raw data for this study. We also thank  Jie Yang  and  Kwansupa Srisombut  for sharing leaf trait data of tree species, and  Nisa Leksungnoen  allowed us to use the SPAD-502 chlorophyll meter. We are pleased to acknowledge the support of the  Department of National Parks, Wildlife and Plant Conservation (DNP)  for permission to collect data in Khao Yai National Park and carry out the fieldwork. The  Faculty of Environment, Kasetsart University , provided a place and equipment for obtaining seed size data. This is publication ISEM 2022-044 SUD. We appreciate two reviewers for their thoughtful comments and efforts on our manuscript.
We present a new finite-sample analysis of M-estimators of locations in a Hilbert space using the tool of the influence function. In particular, we show that the deviations of an M-estimator can be controlled thanks to its influence function (or its score function) and then, we use concentration inequality on M-estimators to investigate the robust estimation of the mean in high dimension in a corrupted setting (adversarial corruption setting) for bounded and unbounded score functions. For a sample of size n and covariance matrix Σ, we attain the minimax speed T r(Σ)/n + Σ op log(1/δ)/n with probability larger than 1δ in a heavy-tailed setting. One of the major advantages of our approach compared to others recently proposed is that our estimator is tractable and fast to compute even in very high dimension with a complexity of O(nd log(T r(Σ))) where n is the sample size and Σ is the covariance matrix of the inliers and in the code that we make available for this article is tested to be very fast.
Introduction Recently, there have been an increase in the quantity and dimensionality of data one has to investigate in Machine Learning tasks. Big datasets are rather difficult to handle because it is not possible anymore to check by hand some gigabyte or terabyte of data to search for possibly abnormal data, and the task is even more difficult in high dimension. An answer to this problem is robust statistics and in particular, because mean estimators are used everywhere in Machine Learning (for empirical risk minimization, cross validation, data preprocessing, feature engineering. ), it has become critical to understand in depth robust mean estimators. In this article, we try to understand one type of mean estimators: M-estimators. We show that they are nearly optimal in the heavy-tailed setting and we exhibit an algorithm that allows us to compute these estimators in a linear number of steps.
In Section 4, we provide bounds on the bias T (P ) -E[X] and on the variance terms in the concentration inequality from Section 3. Bounding the bias has often been a problem in robust statistics, if the distribution is skewed and the bias is not controlled we can only say that we estimate a quantity meant to quantify a central tendency of P but we do not estimate E[X] directly. However in statistical learning for example, estimating the mean is not just an arbitrary choice and we don't want to estimate a central tendency of the dataset, we want to estimate its mean. In this article, we give explicit bounds on the bias and we use those bounds (in Section 5) to give concentration results on T (X n 1 ) around E[X] in the context of heavy-tailed and adversarially corrupted datasets even beyond the L 2 case.
In a corrupted setting where inliers have a q th finite moment, for q > 2, we control the deviations of M-estimators. Let us suppose that the data are an adversarial ε n corrupted dataset (see  Assumption 1). Then, under some assumptions on ψ, for all 0 < t n √ Σ op E[ X-E[X] q ] 1/q , with probability larger than 1 -8 exp(-t), E[X] -T (X n 1 ) Tr(Σ) + Σ op t √ n E [ X -E[X] q ] 1/q ε 1-1/q n. See Proposition 2 below for the formal and more precise statement in the case of Huber's estimator, and see Section 5 for other examples in particular for M-estimators with unbounded score function. In the heavy-tailed setting where ε n = 0, the bound is almost minimax optimal, the difference is that t is allowed to be up to n √ Σ op E[ X-E[X] q ] 1/q instead of the usual t n. This estimator constitutes one of the few tractable and efficient estimators of the multivariate mean in heavy-tailed setting.
Application to the concentration of M-estimators around the mean in corrupted datasets In this section, we investigate the concentration of the three M-estimators taken as example in this article in a corrupted, heavy-tailed setting. The goal will be to recover deviations similar to the one we would have in a Gaussian setting, but when the data are not Gaussian. The gold standard in this context is the deviation of the empirical mean in a Gaussian setting (see  [4] ). If X 1 ,. , X n are i. i. d from N (μ, σ 2 ) for some μ ∈ R and σ > 0, then for all t > 0, P 1 n n i=1 X i -μ > σ t 2n ≤ e -t. ( 17 ) An equivalent of this in the multi-dimensional setting is Hanson-Wright inequality  [21] : let X ∼ N (μ, Σ) for Σ a positive definite matrix, μ ∈ R d. Then, for any t > 0, P ⎛ ⎝ 1 n n i=1 X i -μ 2 > 2T r(Σ) n + 9t Σ op n ⎞ ⎠ ≤ e -t.
Illustrations To illustrate the behavior of M-estimators in heavy-tailed setting we consider a multivariate Pareto law for which the coordinates are drawn, independently of each other, from a Pareto distribution and a multivariate student distribution. All of the dataset present a finite variance but infinite third moment. is the multivariate student distribution with mean μ and degree of freedom ν. In this dataset, there is also two corrupted samples situated in 3001 d. Dataset 4: Data i. i. d from the mixture 0. 4T (0, 3) + 0. 6T (21 d , 3). Uncorrupted dataset.
Coordinates drawn from a Pareto with shape parameter α = 2. 1 and scale parameter 1, with two corrupted samples situated in 3001 d where 1 d is the vector with all coordinates equal to 1. Dataset Coordinates drawn from a Pareto with shape parameter α = 3 and scale parameter 1. Uncorrupted dataset. Dataset Data i. i. d from a mixture 0. 4T (0, 2. 1)+0. 6T (21 d , 2. 1), where T (μ, ν).
Dataset 1 : 2 : 3 : 123 Coordinates drawn from a Pareto with shape parameter α = 2. 1 and scale parameter 1, with two corrupted samples situated in 3001 d where 1 d is the vector with all coordinates equal to 1. Dataset Coordinates drawn from a Pareto with shape parameter α = 3 and scale parameter 1. Uncorrupted dataset. Dataset Data i. i. d from a mixture 0. 4T (0, 2. 1)+0. 6T (21 d , 2. 1), where T (μ, ν).
Amniotic band syndrome (ABS) and limb body wall complex (LBWC) have an overlapping phenotype of multiple congenital anomalies and their etiology is unknown. We aimed to determine the prevalence of ABS and LBWC in Europe from 1980 to 2019 and to describe the spectrum of congenital anomalies. In addition, we investigated maternal age and multiple birth as possible risk factors for the occurrence of ABS and LBWC. We used data from the European surveillance of congenital anomalies (EUROCAT) network including data from 30 registries over 1980-2019. We included all pregnancy outcomes, including live births, stillbirths, and terminations of pregnancy for fetal anomalies. ABS and LBWC cases were extracted from the central EUROCAT database using coding information responses from the registries. In total, 866 ABS cases and 451 LBWC cases were included in this study.
, 2002; Orioli et al. , 2003; Syvänen et al. , 2021; Tinker et al. , 2020; Werler et al. , 2009). In this study, we used data from the European surveillance of congenital anomalies (EUROCAT) network to determine the prevalence of ABS and LBWC in Europe from 1980 to 2019. In addition, we described the spectrum of congenital anomalies that are present in ABS and LBWC patients and presented information on pregnancy characteristics. Finally, we investigated maternal age and multiple births as possible risk factors for the occurrence of ABS and LBWC.
| EUROCAT network EUROCAT is a European network of population-based congenital anomaly registries which was founded in 1979  (Tucker et al. , 2018). All registries submit their standardized data once or twice a year to the Joint Research Centre (JRC)-EUROCAT Central Registry using a secure portal (EUROCAT Central Registry, University of  Ulster, 2013; Kinsner-Ovaskainen et al. , 2018). Cases with congenital anomalies are identified from various sources, for example, hospital discharge records, birth certificates, and autopsy results (https://eu-rd-platform. jrc. ec. europa. eu/eurocat/eurocat-members/registries_en). Live births (LB), stillbirths (SB), fetal deaths (FD) with a gestational age of 20 weeks or more, and terminations of pregnancy for fetal anomaly.
| RESULTS In total, 866 ABS cases and 451 LBWC cases were included in this study. The prevalence for years 1980-2019 was 0. 53/10,000 births for ABS and 0. 34/10,000 births for LBWC (Table  1 ). ABS was least prevalent between 1980 and 1989, while LBWC was least prevalent between 1980 and 1999. The first ABS cases were registered in 1980, but few registries had data before 1990 (only 35 ABS cases were registered in 1980-1989). After 1990, the prevalence was stable around 0. 54/10,000 births (the 95% CIs of the three 10-year periods all overlap). The first LBWC case was registered in 1988 and only 23 cases were registered between 1980 and 1999. From 2000 onwards, the LBWC prevalence was stable around 0. 37/10,000 births (with overlapping 95% CIs for the two 10-year periods). Between 2000 and 2019, the prevalence of both ABS and LBWC was higher in the UK registries compared to the non-UK registries, with more than half of the LBWC cases recorded in the UK registries (Table  S1 ).
A detailed description of the participating registries can be found on the EUROCAT website (https://eu-rd-platform. jrc. ec. europa. eu/ eurocat/eurocat-members/registries_en). Based on the questionnaire coding information responses, data were extracted from the JRC- EUROCAT central database on December 17, 2019, for birth years 1980-2017 and a second data extraction was completed on January 18, 2022, for birth years 2018 and 2019: 1. ABS extraction: ICD9-BPA code 7628 or 880013 or 755504 or 755605 or ICD10-BPA code Q798 or Q719 or Q729 or Q738 or Q8687 or Online Mendelian Inheritance in Man (OMIM) code 217100 or ORPHA code 295000 or a text description of ABS or one if its synonyms in the different languages.
Altimetry data and eddy tracking To investigate mesoscale activity off northeast Brazil, we used altimetry observations including absolute dynamic topography (ADT) and derived surface geostrophic velocities data available from January 1993 to December 2018. These data were produced by Ssalto/DUACS multi-mission products with support from CNES (http://www. AVISO. altimetry. fr/duacs/). Multi-mission products refer to the combination of all satellites data available at one time: Jason-3, Sentinel-3A, HY-2A, Saral/AltiKa, Cryosat-2, Jason-2, Jason-1, T/P, ENVISAT, GFO, and ERS1/2. This dataset is mapped daily onto a 0. 25°× 0. 25°latitude/longitude grid  (Ablain et al. , 2015; Dupuy et al. , 2016)  and distributed by the Copernicus Marine Environment Monitoring Service (CMEMS: http://marine. copernicus.