dataesr/scientific-paragraphs-categorization huggingface dataset:
It also performs better when the dis-tribution of the studied species tends to be continuous, with individuals geographically close and more prone to share ancestral genotypes  (Durand et al. 2009 ). In the latter case, individuals are assigned to the most likely cluster on the basis of multi-locus genotypes sampled at distinct geographical locations without assuming predefined populations  (Du rand et al. 2009 ). Here, the specified spatial prior is weak compared to the amount of information contained in the molecular dataset, and therefore, deviations from spatial smoothness are still al lowed in the posterior inference  (Corander et al. 2008 ). We added UTM coordinates for each individual and ran 20 replicates of the admixture model for each value of the maximal number of clusters (K). The best value of K was examined from 1 to 10 with a maximum number of 10 000 iterations per run (20 repetitions) and a tolerance value of 10 -7.
SNP dataset The regional dataset initially comprised a total of 5124 bi-allelic SNP loci in 73 reef manta rays from 4 locations in New Caledonia and 20 individuals from East Australia (Table  1 ). One individual (MA138) from East Australia was removed due to a call rate of less than 80%. The local dataset had a total of 3973 bi-allelic SNP loci in 65 reef manta rays from 3 locations in New Caledonia. One of the sites (Pouembout) was removed from the finescale analysis due to the small sample size (n = 8) and difference in habitat type compared with the rest of the sites. After the filtering, the total number of polymorphic SNP loci was 3619 for 92 individuals in the regional dataset and 2676 for 65 individuals in the local dataset. All filtering steps are sum marized in Table  1. The mean and distribution of heterozygosity was similar among individuals within sample sites and regions with no outliers (Fig.
Genetic diversity Genetic diversity statistics for all sample partitions are shown in Table  2. H e was significantly higher for the East Australian outgroup (t 3978 = 6. 35, p < 0. 001) compared with the overall New Caledonian dataset while no significant difference was observed for A R (t 3978 = 0. 76, p > 0. 05) (Table  2 ). When considering the local dataset, there were no significant differences in any genetic diversity statistics between sample partitions within New Caledonia (F = 0. 05, p > 0. 5, Table  2 ). Inbreeding coefficients (F IS ) were relatively high at all levels of population structure, and all were significantly different from zero (p < 0. 001) (Table  2 ). At the sample site level, the average proportions of heterozygosity in individuals were significantly different (t 3816 = 2.
18, p < 0. 05) between samples from East Australia (mean ± SD, F IS = 0. 0662 ± 0. 03020) and individuals from all groups of New Caledonia in the regional dataset (F IS = 0. 0844 ± 0. 02093). Among groups from New Caledonia, values ranged from 0. 0699 ± 0. 02762 in Noumea to 0. 0812 ± 0. 02918 in Ouvea, with no significant differences among sites in the local dataset (F = 0. 93, p > 0. 05).
Population differentiation and structure 3. 3. 1. Pairwise F ST and Nei's genetic distance D Statistically significant genetic differentiation was observed between all sampling sites (Table  3 ). The highest estimates of genetic differentiation were found between East Australia and New Caledonia (F ST = 0. 0958 ± 0. 006 SD, p < 0. 001, Table  3 ). The degree of differentiation between New Caledonian sampling sites was lower but still statistically significant (pairwise F ST = 0. 0116-0. 0165, Table  3 ). Overall F ST values for the regional and local datasets are provided in Table  3.
DAPC Examination of the mean α-score values revealed that the appropriate number of PCs was 30 for the regional dataset and 21 for the local dataset (Tables  S2  & S3 ). The DAPC showed individuals clustered into their country and sampling site of origin with high confidence (Tables  S4  & S5 ). Considering the regional data set first, DAPC revealed a clear distinction across all samples from East Australia and New Caledonia, with PC1 providing clear discrimination (Fig. 2 ). Using the classification algorithm, all individuals had a 100% probability of being assigned back to their country of origin (Table  S5 ). Considering the local dataset, the first of the two DA eigenvalues represented most of the variation within this dataset (Fig. 3 ). The DAPC discriminated sites along both the north-south (PC1, discriminating Noumea) and east-west axes (PC2, differentiating Ouvea and Touho).
Estimation of spatial structure: TESS3 analysis For the regional dataset, the TESS3 analysis revealed the most likely number of ancestral populations/genetic clusters or best value of K as 4 (the cross-validation test exhibited a minimum value or a significant plateau at K = 4) (Fig. S3 ), supporting subtle levels of population structure  (Caye et al. 2016). At K = 2, a strong distinction is revealed between Australian individuals and all individuals from New Caledonia, with distinct admixture compositions with 2-5 and 0-9% ancestry proportions of each other's dominant cluster, respectively. At K = 4, each site has its own distinct admixture composition, with a dominant ancestral population (Fig. 4 ). On average, New Caledonia individuals have less than 1. 4 ± 0. 003% ancestry proportions of the dominant East Australia cluster.
Gene flow analysis Relative directional migration rates were estimated using 3 differentiation metrics, but, as the patterns were consistent, only Jost's D results are presented (Fig. 5 ). The values displayed in the figures do not represent the magnitude of each mi gration (as a Fig. 2. Inference of population structure of reef manta ray Mobula alfredi from East Australia and New Caledonia (regional dataset) using 3619 single nucleotide polymorphisms: plot of individuals based on the first 2 principal components (PCs) of the discriminant analysis of principal components (DAPC). The inset shows the eigenvalues of the analysis, representing the proportion of genetic structure captured by each of the PCs. Black PCs were used in the DAPC whereas greyed out ones were not measurement of the proportion of migrants per generation would) but compare migration rates proportionally to the highest, which always equals one.
Acknowledgements. We thank Conservation International, the Aquarium des Lagons, and The Manta Trust for their overall support. We especially thank Franck Bouilleret, Hugues Gossuin, and Thomas Auger for their invaluable assistance in field operations, as well as Rochelle Constantine, Olivier Chateau, and François Tron for their help and support. We also thank the Hô-üt Association and all of its members, and all those involved in the field operations for their valuable help (Ludovic Mazens, Marie-Renée Pabouty, Amaury Durbano, Jean-Baptiste Badiou, Taiki Kido, and the team of divers from the Aquarium des Lagons). We finally gratefully acknowledge the spokespersons for customary authorities of Ouvéa and Touho, and the 3 Provinces for allowing such operations. H.
L. was supported by a scholarship from the Southern Province of New Caledonia, E. L. C. was supported by a Rutherford Discovery Fellowship from the Royal Society of New Zealand Te Apārangi. Laboratory and field work were funded by the Keidanren Nature Conservation Fund (KNCF) through the SATO YAMA UMI Project and Conservation International. Australian sample collections were funded through the Australian Research Council Linkage Grant: LP150100669. Sample collection was conducted with authorizations from the Southern Province (permit no: 34584) and the Northern Province (permit no: 609011-33) of New Caledonia. In the Loyalty Islands Province, no permit was required by the local authorities, although permission was granted by the local spokespersons for customary authorities. Transportation of tissue samples from New Caledonia to New Zealand was conducted with the authorization of the CITES authorities under permit no.
Number of single nucleotide polymorphism (SNP) loci initially found (Initial SNP dataset) using Diversity Arrays Technology (DArT) sequencing in reef manta ray Mobula alfredi samples from both East Australia and New Caledonia (Regional dataset) and from only New Caledonia (Local dataset). Also shown are the numbers of loci and individuals retained as different quality control filters were applied to the dataset. The final datasets used in the subsequent population genetic analyses are shown in the fully bold row and for genetic diversity analyses are shown in the row in italics.
Acknowledgements. AA is supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No. 838776. ET is supported by an  STFC  PhD studentship. DW is supported in part by the  STFC  Consolidated Grant  ST/T000791/1  and the  EPSRC New Horizons  Grant  EP/V049089/1. We acknowledge the  Mainz Institute for Theoretical Physics (MITP) of the Cluster of Excellence PRISMA+  (Project  ID   39083149 ) for hospitality and support during part of this work. Note added. Edward Tasker passed away in January 2020. He made major contributions to this work during his PhD studies at  Imperial College London. Ed was a much-loved colleague and friend, and a gifted physicist and mathematician with a seemingly endless supply of puns and a knack for solving problems in unexpected ways.
Background To date information of more than 660 million reference single nucleotide polymorphisms (refSNPs) and 5. 9 million regions with structural variation (SV) on the human DNA are known and stored in the publicly available databases, like dbSNP [1]. To identify those genetic variants, that are associated with common human diseases, genome-wide association studies (GWAS) can be conducted. Usually, commercial single nucleotide polymorphism (SNP) microarrays are used to carry out genotyping of DNA samples for these studies. There are two predominant companies for high throughput genotyping arrays, Thermo Fisher Scientific Inc. , Santa Clara, CA (Affymetrix ™ ) and.
However, the standard outputs of common imputation programs (e. g. Beagle or IMPUTE2) contain different metrics. Hence, choosing an imputation program binds the user to the metrics provided, although the SNPTEST program offers the option of calculating a measure similar to that of info  [19]. We propose a new pair of metrics to depict additional aspects of imputation accuracy also calculable from dosage files. First, we aim to quantify the amount of individual-specific versus population-specific genotype information in the imputed genotypes. Second, we aim to assess the heterogeneity between dosages of a marker across the sample at hand. Both measures can be used to identify markers or regions in which populationspecific genetic information conceal individual-specific information and are therefore less informative for e.
Defining thresholds for marker filtering In order to exclude less accurately imputed variants from further analysis, one needs to define a meaningful and applicable threshold for any accuracy index. For the measure info threshold values like 0. 8 or 0. 3 have been proposed, but without sound justification  [5, 20, 21]. It was even proposed to lower the threshold for info in very large samples, as those of the UK Biobank, and still maintain a good ability to detect associations  [22]. We applied robust regression (PROG ROBUSTREG of SAS 9. 4; cut-off-α = 10 -6 for leverage points, cut-off-multiplier = 5 for outliers  [23] ) to estimate the expected value of Iam and hiQ and their variance-covariance matrix, assuming a hidden two-dimensional normal distribution (ignoring the upper bounds of the indices).
Nevertheless, it is still hard to visually find regions that are enriched with less accurately imputed markers. To identify more genomic regions prone to host inaccurate markers we calculated the exponentially weighted moving averages (ewma) of Iam and hiQ (PROC EXPAND of SAS 9. 4; smoothing factor 0. 1)  [23]. We consider variants with an ewma < threshold (0. 47 for Iam and 0. 97 for hiQ) as belonging to a "hot region" and variants with an ewma < threshold/2 (0. 23 for Iam and 0. 48 for hiQ) as belonging to a "very hot region". Across the whole genome, we were able to identify 4,603 "hot regions" and 171 "very hot regions" according to Iam HWE , as well as 2,899 "hot regions" according to hiQ. These regions partially overlap or are interconnected. "Hot" and "very hot" Iam-regions contain in total 85,790 variants, only about 8‰ of all variants.
The H-distance quantifies the distance between two (trinomial) probability distributions, taking value H = 0 in case of coincident probability distributions and H = 1 if the probability vectors are perpendicular  [27, 28]. Therefore, we defined In Table  4  the "average of best guess dosages" for marker 1 f bg g = 1 0 0 compared to the average dosages ( f ad g = 0. 6 0. 3 0. 1 ) yields an hiQ = 1 -1 -√ 0. 6 = 0. 53. This indicates a loss of heterogeneity between dosages and reduced power of a statistical test. For marker 2, where f ad g = f bg g , hiQ takes on the value 1, indicating fully achievable inter-individual heterogeneity and no reduced power of a statistical test. A SAS ® macro far calculating Iam HWE , Iam chance and hiQ based on the dosage-file as output of IMPUTE2 is included in the Additional file 1.
Application to a sample of lung cancer patients and controls We applied the novel indices to a dataset of the Integrative Analysis of Lung Cancer Etiology and Risk program of the International Lung Cancer Consortium (INTEGRAL-ILCCO) to examine the behaviour of Iam hiQ, to find appropriate thresholds for marker filtering and for comparison with an established accuracy measure. The sample comprises 14,803 lung cancer cases and 12,262 controls of European descent. They were genotyped on the OncoArray, which queried 517,482 SNPs. The array is designed to cover the whole genome (with GWAS backbone) and for fine mapping of susceptibility to common cancers as well as for de novo discovery, and hence is enriched with low frequent and rare variants  [29]. About 50% of markers are considered as GWAS backbone.
Details of the sample, the genotyping and the quality control are given elsewhere  [30]. The OncoArray whole-genome data were imputed in a two-stage procedure, using SHAPEIT to derive phased genotypes and IMPUTEv2 to infer additional genotypes for genetic variants included in the 1000 Genomes Project (phase 3 panel)  [6, 15]. We restricted calculations and comparisons to markers of the autosomes. A total of n = 10,427,599 SNPs hiQ = 1 -1 -  were finally included in this quality assessment. Presumably difficult to impute, due to their MAF, are 5,226,623 of these SNPs (50%) with a MAF lower than 1% and 1,500,835 SNPs with a MAF between 1 and 5% (Additional file 2).
Acknowledgements We acknowledge support by the  Open Access Publication Funds of the Göttingen University. Vanderbilt University Medical Center, Nashville, TN, USA. 13 ISPA  and  CIBERESP, Faculty of Medicine, University of Oviedo, Oviedo, Spain. 14 Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel. 15 Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, The University of Liverpool, Liverpool, UK. 16  Departments of Health Evidence and Urology, Radboud University Medical Center, Nijmegen ,  The Netherlands. 17 Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, WA, USA. 18 National Institute of Occupational Health, Oslo, Norway. 19 British Columbia Cancer Agency, Vancouver, BC, Canada.
Scanning Laser Vibrometer Measurements The structural response is measured using a scanning laser vibrometer situated in an adjacent room and protected by a glass window (see Figure  5 ). Scanning laser vibrometry enables non-intrusive measurements (no mass added to the surface) and a large number of measurement points. This large amount of experimental data proves to be useful for the validation of the SEA model-the responses of which are averaged in space. However, measurements can only be performed on the external surfaces that are visible from the adjacent room through the protective window. Two configurations of the structure were set up. In the vertical configuration, shown in Figure  3 , the response of the external envelope was measured, while the response of the bottom was measured in the horizontal configuration, shown in Figure  5.
The traditional approach to SEA modeling typically involves the description of a complex system in terms of a number of simpler connected subsystems. These SEA subsystems are usually chosen in a fixed formulation library, in order to fit the real geometry as closely as possible. However, traditional SEA codes often have somewhat limited libraries of subsystems that are based on analytical derivations of wave propagation and scattering in simplified cross-section. Such cross-sections are adequate for simple systems; however, they are often not general enough to rigorously describe the vibroacoustic behavior of complex structures. The results presented in this article were obtained with Wave6 software, where a statistical energy analysis formulation using finite element and periodic structure theory, called hybrid FE-SEA periodic theory, was implemented  [21, 30, 32].
Test-Simulation Comparison Figure  19  shows the mean RMS acceleration of each subsystem in the third octave bands. The simulated results (blue lines) are compared with experimental data obtained by laser vibrometry measurements (black lines with stars). The time acceleration response is measured at several points of each subsystem (46 and 20 measurements on a line along the x-axis for the subsystems SEA 2D 1 and SEA 2D 2, and 175 measurements covering all the surface for the bottom subsystem SEA 2D 3). The Power Spectral Density is then computed for each point. Then, for each subsystem, a spatial average of the PSD of all points belonging to the subsystem is performed. As explained in Section 2. 3, with scanning laser vibrometry, the measurements at different points are not synchronous but sequential, allowing only the computation of the PSD S i and not the cross power spectral densities S ij between two points i, j.
, 2012; Baby et al. , 2018; 2019). Sections that have continuous coverage from the shoreline to the nearly flat basin floor-typically reached at a distance of between 300 and 600 km from shore on the SAM-are essential to constraining the extent to which the long-distance sediment transport dynamics in our model adequately describe the development of passive margin stratigraphy. Seven seismic sections interpreted by  Baby et al. (2018; 2019)  comprise the dataset that we will use to test the two models and determine optimal model structure and parameter values (Figure  2 ). We omit one of their sections-their section 2 (Figure  2 )-from our analysis because it is by far the shortest (< 500 km) and because at its end point there are deposits approximately 3 km thick. It is not possible to evaluate models for long-distance sediment transport using section 2 because the section ends before deposits reach a negligible thickness.
Because parameter values represent the contributions of slope bypass and long-distance transport processes, best-fit parameter values reveal the importance, or lack thereof, of those processes to passive margin evolution. For our inversions we used the ABC-SMC (approximate Bayesian computation-sequential Monte Carlo) algorithm implemented in PyABC  (Klinger et al. , 2018) , an open-source Python package that allows efficient parameter estimation using the iterative procedure described above. See  Sisson et al. (2007)  and  Toni et al. (2009)  for details of ABC-SMC approaches, and Table  S1  for algorithm parameters used in our study. Preprint of article published in Basin Research, https://doi. org/10. 1111/bre. 12698 There are many choices that govern inversion behavior, including the choice of the algorithm itself.
Our chosen approach is purposefully similar to genetic algorithm methods used in prior efforts to infer parameters of SFMs (e. g. ,  Lessenger and Cross, 1996; Bornholdt and Westphal, 1998; Bornholdt et al. , 1999; Cross and Lessenger, 1999; Imhof and Sharma, 2006; 2007; Falivene et al. , 2014; Yuan et al. , 2019a) , but differs in the details of how successful parameterizations are selected from each generation and perturbed to produce the next. Exploratory testing of different parameter inference algorithm choices did not lead to meaningfully different results. Conducting such an inversion exercise requires estimating or assuming initial and boundary conditions for the model that cannot be precisely known from geophysical and stratigraphic data (for example, the subsidence history of the basin floor over the past 130 Ma).
The set of possible misfit functions for an inverse problem is infinite, necessitating somewhat arbitrary choices. Our misfit functions are purely geometric-that is, they use deposit shape alone. This is appropriate given the simplicity of our model, but we note that additional constraints such as sand percentages derived from well-log data can allow the inference of Preprint of article published in Basin Research, https://doi. org/10. 1111/bre. 12698 additional model parameters (e. g. ,  Falivene et al. , 2014). Other options for constructing misfit functions include comparing deposit thickness or geometry at only a few key points (e. g. ,  Yuan et al. , 2019a)  or, if working in more than one planview dimension, comparing metrics of planview margin geometry like the shelf edge  (Zhang et al.
, 1992; van Balen et al. , 1995) , such an approach may ultimately be counterproductive when we expand our view to include the distal portion of the margin because it yields models that Preprint of article published in Basin Research, https://doi. org/10. 1111/bre. 12698 cannot transport sediment far enough from shore without some other process or additional changes in erodibility with depth or distance from shore. The local, linear model provides, for all sections that can be reasonably fit by either approach, a worse fit to the modern bathymetric surface than was obtained with the nonlocal, nonlinear model (Figure  7, 8 ). While best-fit parameterizations of the local, linear model do exhibit sediment delivery to the distal portions of the sections (achieved through large erodibility decay depths that yield non-negligible erodibility at depth), this comes at the cost of model-data fit in the nearshore environment.
org/10. 1111/bre. 12698 because the data requires that good-fitting models be able to distribute sediment to the distal portion of the basin even relatively early in the margin's evolution when there do not yet exist the slopes required to drive sediment bypass in the absence of another mechanism for longdistance transport. The critical slope of non-deposition (Figure  9B ) remained remarkably consistent between the surface-only and multiple-reflector inversions, suggesting that the model most effectively adjusts to the need to deliver early deposits far from shore with changes in the travel distance, which affects transport over all slopes, rather than the critical slope, which only affects transport over meaningful gradients. Physically this may indicate the importance of longrunout sediment transport processes (e.