dataesr/scientific-paragraphs-categorization huggingface dataset:
Errors estimated at intermediate stages of the dataset preparation (e. g. , RMSE reported by photogrammetry and laser-scanning software) can sometimes be misleading, as they may not include all possible error sources, and hence cannot be used confidently to ascertain consistency between topographic and bathymetric products forming this long-term dataset. Final DEM quality was assessed in terms of accuracy, precision and reliability through fit-for-purpose experiments. Accuracy or bias, reported as the mean error, and survey precision, reported as the standard deviation of error, were estimated in comparison with a reference DEM (ground truth) comprising the most stable parts of the study site (cf. explanation below). Using the comparison with a ground truth also enabled to detect eventual tilts (i.
07 m (determined as one standard deviation between independent measurements, averaged over all cells). Similar results were obtained for the topographic counterpart, but due to the large difference in area covered, the ground truth accounted for just over 10,000 truth elevations (cf. Table  6 ). The at-a-cell vertical precision is a good proxy for the overall precision of the dataset, as it characterises the ability to effectively replicate measurements over time. Using the ground truths, DEMs that initially presented a vertical bias (>0. 1 m) but high precision, suggesting a systematic and easily rectifiable shift in elevation, generally owing to improper setting of the vertical datum, were corrected, effectively reducing registration errors across the dataset (Table  6 ). DEMs identified as presenting large and complex errors (e.
g. , tilts) are not included in the dataset. This essentially concerns photogrammetric DEMs collected prior 2014. Final error statistics are provided in the metadata accompanying DEMs and are presented in Online-only tables for this paper. The evaluation shows that all survey methods were able to produce high quality data, shown by bias and precision mostly below 0. 1 m. No clear degradation of data quality resulting from measuring submerged topographies can be observed. Note that because GPS DEMs are limited to sand, where morphological changes occur, this prevented us from using the data validation procedure presented above. Where possible, a GPS DEM was compared with a synchronous (i. e. , same day) cross-shore profile, also providing information on mean deviations, precision and tilt. Waves.
The continuous hourly wave dataset for Porsmilin is comprised of HOMERE (01/2000 -12/2016) and NORGAS-UG (01/2017-12/2019). To avoid discontinuities in the time-series, NORGAS-UG data were corrected for bias, based on the comparison between HOMERE and NORGAS-UG at the node point 47554 (Table  7 ). As demonstrated in previous work, HOMERE and NORGAS-UG have been validated at both global and regional levels using a combination of in-situ measurements, remote sensing from satellite altimeters and outputs from the NOAA/NCEP configuration of WW3  63, 64. We further assessed the capability of these wave hindcasts to provide realistic offshore and inshore conditions for Porsmilin by comparing hindcast data at the nearest grid point to available wave buoy measurements (http://candhis. cetmef. developpement-durable.
gouv. fr/) at Pierres Noires and Porsmilin (WW3 nodes 47039 and 47562, respectively). The Pierres Noires buoy (Fig. 1b, d )   The results of the validation for all four wave parameters (Hs, Tp, T02 and Dir) at both offshore and inshore locations, using the Pearson correlation coefficient R, mean bias (estimated as 'hindcast -measured data' for model-buoy comparisons and 'NORGAS-UG -HOMERE' for model inter-comparisons) and RMSE, are presented in Table  7. HOMERE and NORGAS-UG both demonstrate overall excellent skill modelling offshore and inshore waves, particularly for Hs, with R values constantly above 0. 97. Agreement with in-situ data decreases slightly for Tp, T02 and Dir. The latter was observed in previous research  22, 63, 64  and explained by the differences in accounting for mixed sea states (i.
Usage Notes Field surveys. Using the dataset, subaerial sediment dynamics from event to pluri-annual time scales can be assessed through sediment budget analyses, complemented since 2008 by approximately bi-annual bathymetric surveys capturing sediment transfers with the shoreface. Due to relocation of the profile head in 2014, profile comparisons are best performed by excluding the first 20 meters or so, as this section of the beach was not measured before 2014. Likewise, quantification of back beach and foredune-embankment changes will benefit from analysing separately the surveys undertaken before and after relocation of the profile head. DEMs provide very detailed maps of beach morphology and allow for numerous cross-shore and alongshore profiles to be extracted. DEMs are provided on a constant grid making for easy computations of temporal changes without requiring intermediate steps, while format selection ensures that DEMs are readily usable with a variety of GIS and scientific programming software.
ifremer. fr/en/resultats/niveaux). Likewise, analysis of shoreline water levels requires computing the total run up at the shore from the wave hindcasts provided and adding tidal levels, which may be done using equations presented in ref. 65. complementary datasets. A seamless subaerial and subtidal LiDAR DEM  66   , determined using all DEMs of the same method. Individual error statistics from which these values are derived are presented in Online-only tables. Bias is the mean deviation (calculated either as 'measurement -ground truth' or 'bathymetry -topography') expressed in absolute value, thus representing the actual magnitude data can be offset from a reference level, regardless of the direction. (HOMERE  57  ) and https://sextant. ifremer. fr/record/0873e969-6c97-4405-a040-fd4599f5c936 (NORGAS-UG  58  ).
Dataset-Astronomical tides. Time is relative to 24 h Central European Time -CET. Lat/Long are referenced to WGS84. Tidal levels are expressed both with reference to the chart datum (i. e. , the tide gauge reference corresponding to the lowest astronomical tides) and to the national elevation datum NGF-IGN69. hasrecordedbi-hourly wave estimates in 60 m water depth since October 2005. Between March and April 2004 (period not covered by NORGAS-UG), a directional wave buoy was installed temporarily offshore Porsmilin (Fig. 1c). Intercomparing outputs from HOMERE and NORGAS-UG taken separately enabled assessment of the models' reliability over the common period of hindcast (i. e. , 01/2008-12/2016, n = 78,912).
obtained between 2011 and 2013 can be accessed at https://diffusion. shom. fr/pro/risques/litto3dr-finistere-2014. html. Wave data for other hindcast nodes can be accessed at https://doi. org/10. 12770/cf47e08d-1455-4254-955e-d66225c9dc90 Validation of the DEM dataset for the different survey methods. All values are presented as.
acknowledgements Since its beginning in 2003, the continuing Porsmilin beach survey program has been funded by the  Institut Universitaire Européen de la Mer (IUEM/Univ. Brest) via the Observatoire du Domaine Côtier (ODC) , as well as the national coastal observation service SNO-DYNALIT (https://www. dynalit. fr/). DYNALIT acknowledges funding from the  National Centre for Scientific Research (CNRS-INSU) , the  French coastal research infrastructure (IR-ILICO) , the  French  ministry for higher education, research and innovation and the  French agency  for research on the environment. Since 2014, the purchase, maintenance and utilisation of the terrestrial laser-scanner VZ-400 have been shared with  LETG Laboratory at Univ. Caen , whose help is gratefully acknowledged. Acquisition of the survey equipment (echo sounders, TLS and drones) was possible thanks to a number of co-funding sources, including  CPER, IUEM's Pôle Image et Instrumentation  and  PIA1  through the  EquipEx CRITEX.
The Pierres Noires and Porsmilin wave buoy data used for wave hindcast validation are funded and administered by  Cerema  (http://candhis. cetmef. developpement-durable. gouv. fr/). Tide measurements for Le Conquet are provided by the  French naval hydrographic and oceanographic service (Shom). Ifremer is acknowledged for undertaking and providing HOMERE and NORGAS-UG databases. The authors wish to extend their gratitude and thanks to everyone who has contributed to the  Porsmilin beach survey program , and to the anonymous reviewers for their helpful suggestions.
BEACH PROFILE DATASET Folder Origin and orientation [Lat/Long/degN] Data File Time- series File format Profile_topo 48°21'21. 946" N 4°40'46. 173" W 205. 763°y yyyymmdd_Porsmilin_SurveyMethod_Dist_IGN69_0. 5_ yyymmdd_Porsmilin_GPS_Dist_IGN69_0. 5_P. txt DP. txt 2003-2019 Column 1 -Chainage (m) Column 2 -Elevation (m NGF) Profile_bathy Same as above yyyymmdd_Porsmilin_MES_Dist_IGN69_0. 5_DP. txt 2008-2019 Same as above Profile_fusion Same as above yyyymmdd_Porsmilin_FUS_Dist_IGN69_0. 5_DP. txt 2008-2019 Same as above.
Validation of HOMERE and NORGAS-UG wave hindcast datasets. The first three lines correspond to model-buoy comparisons. CANDHIS wave buoys at Pierres Noires and Porsmilin correspond to hindcast nodes 47039 and 47562, respectively. The bottom three lines correspond to comparisons between HOMERE and NORGAS-UG over the common period of hindcast. WAVE HINDCAST VALIDATION Hs Tp T02 Dir Model Node n R Bias rms R Bias rms R Bias rms R Bias rms HOMERE 47039 75,358 0. 98 0. 17 m 0. 32 m 0. 79 -1. 37 s 2. 04 s 0. 89 -0. 20 s 0. 80 s 0. 52 -1. 56°28. 41°N ORGAS-UG 47039 96,273 0. 97 0. 16 m 0. 35 m 0. 76 -1. 70 s 2. 34 s 0. 89 -0. 49 s 0. 89 s 0. 56 -0. 88°27. 92°H OMERE 47562 976 0. 97 0. 02 m 0. 11 m 0. 62 -1. 32 s 2. 42 s 0. 36 -0. 37 s 2. 11 s 0. 30 -2. 16°10. 65°I ntercomparison 47562 78,912 0. 97 0.
The second experimental framework, where the control vector is z = ((θ s ) a , q t ), corresponding to the conservative variables, is named "EXP". The numerical aspects of the 1D-Var minimisation are kept the same as in "REF". Then, a set of reference matrices B x (T, q v ) has been estimated every hour using the Ensemble Data Assimilation (EDA) system of the AROME model on 9 February 2020. These matrices were obtained by computing statistics from a set of 25 members providing 3 h forecasts for a subset of 5000 points randomly selected 3 Numerical Weather Prediction Satellite Application Facility in the AROME domain to obtain a sufficiently large statistical sample. Then, matrices associated with fog areas, and noted B x (T, q v ) f og , were computed every hour by applying a fog mask (defined by areas where q l is above 10 -6 kg kg -1 for the three lowest model levels), in order to select only model grid points for which fog is forecast in the majority of the 25 AROME members.
This result is key for 1D-Var retrievals which are commonly used in the community of ground-based remote sensing instruments to provide databases of vertical profiles for the scientific community. In fact, the accuracy of 1D-Var retrievals is expected to be more robust with less flow-dependent B matrices. It has also been noticed that these background error statistics are less dependent on the diurnal cycle and on the meteorological situation (e. g. in the presence of fog at 06 UTC and low clouds at 21 UTC), contrary to the B x (T, q v ) matrix where there is a reduction in the area of positive correlation in the lowest layers between 06 UTC and 21 UTC (Fig. 4 ). The 1D-Var results are now assessed in observation space by examining innovations (differences between observed and simulated brightness temperatures) from AROME background profiles and residuals.
2 (e)  and (f)), the residual values are largely reduced in the frequency bands sensitive to liquid water for channels 5, 6 and 7, especially for EXP as shown by the comparison of the pixels in the dashed rectangular boxes in  Figs. 5 (b) and (c). Residuals are also slightly reduced for EXP in the morning and during the fog and low temperature period for the first five channels between 2 and 8 UTC. In order to quantify these results on 9 February 2020 dataset (all hours and all channels), the bias and root mean square (RM S) error values are computed for the background and the analyses produced by REF and EXP. The innovations are characterized by a RM S error of 3. 20 K and a bias of 1. 32 K. Both assimilation experiments reduce these two quantities by modifying model profiles. The RM S errors are 0.
The adjoint version of this conversion has been developed for an efficient estimation of the gradient of the cost-function. Dedicated background error covariance matrices have been estimated from the Ensemble Data Assimilation system of AROME. We firstly demonstrated that the matrices for the new variables are less dependent on the meteorological situation (all-weather conditions vs. fog conditions) and on the time of the day (stable conditions by night vs. unstable conditions during the day) leading to potentially more robust estimates. This is an important result as the optimal estimation of the analysis depends on the good specification of the background error covariance matrix which is known to highly vary with weather conditions when using classical control variables. The comparison of the new 1D-Var system with a classical system using temperature, water vapour and liquid water path shows rather similar results in terms of fit to observed T B.
Analytics approach: constraining diffusion kinetics and Effective Diffusion Temperature In this study, we used Matlab codes initially developed by  Tremblay et al. (2014a, b; 2018; 2021, code  available on Zenodo at https://doi. org/10. 5281/zenodo. 5808021) to (1) determine 3 He diffusion kinetics from step-heating experiment data applying a multi-diffusion domain (MDD;  Lovera et al. , 1989 Lovera et al. , , 1991) )  model framework, and (2) numerically simulate 3 He loss for different thermal histories using the sample-specific diffusion kinetics information. The predictive model of 3 He diffusion with time also includes 3 He production by cosmic ray incidence using a 3 He production rate in quartz at sea level and high latitude (SLHL) of 116 at. g -1. yr -1  (Vermeesch et al. , 2009) , scaled to the sample geographic location and elevation according to the non-time dependent scaling scheme of  Stone (2000; Balco et al.
We also compiled all quartz 3 He paleoIsoEDTs available in the European Alps  (Tremblay et al. , 2018; Guralnik et al. , 2018;  this study; Fig. 10 ). Interestingly, while this compilation reveals no apparent relationship with 10 Be exposure age (from LGM to Holocene; Fig. 10a ), we observe an apparent negative correlation between samples paleoIsoEDT and elevation (Fig. 10b ). Furthermore, while samples at low to moderate elevations have paleoIsoEDTs that are relatively consistent with their estimated modern EDTs along an apparent linear lapse rate (around -0. 5°C/100m lapse rate), paleoIsoEDTs recorded in rock surfaces above ~2200 m a. s. l. clearly depart from modern EDTs/lapse rate trend with significantly "colder" 3 He signals. Although the compiled Alpine dataset is still limited, such an observed distribution raises the question of the influence of rock-surface elevation on 3 He signal records.
Acknowledgments This work was supported by the  Swiss National Science Foundation  (Grant  PP00P2_170559 ), with measurements at BGC in part supported by the  Ann and Gordon Getty Foundation. PGV acknowledges support from the  French ANR-PIA program  ( ANR-18-MPGA-0006 ). MMT acknowledges support from the  U. S. National Science Foundation  ( OPP-1935945 ) and the  AAAS Marion Milligan Mason Fund. We thank  Benjamin  and  Fred Lehmann , and  Guilhem A. Douillet  for their help in the field to collect samples.
Code availability The source codes (with examples of input dataset) used to determine (1) 3 He diffusion kinetics from step-heating experiment applying a MDD model framework (example diffusion data from MBTP18-9) and to (2) conduct forward simulation of 3 He production/diffusion along given time-EDT scenario (simplified LGM scenario for MBTP18-1 as example, Fig. 7 ) are available on Zenodo at https://doi. org/10. 5281/zenodo. 5808021  (Tremblay, 2021).
Deep hydrothermal vents are highly fragmented and unstable habitats at all temporal and spatial scales. Such environmental dynamics likely play a non-negligible role in speciation. Little is, however, known about the evolutionary processes that drive population-level differentiation and vent species isolation and, more specifically, how geography and habitat specialisation interplay in the species history of divergence. In this study, the species range and divergence of Alviniconcha snails that occupy active Western Pacific vent fields was assessed by using sequence variation data of the mitochondrial Cox1 gene, RNAseq, and ddRAD-seq. Combining morphological description and sequence datasets of the three species across five basins, we confirmed that A. kojimai, A. boucheti, and A. strummeri, while partially overlapping over their range, display high levels of divergence in the three genomic compartments analysed that usually encompass values retrieved for reproductively isolated species with divergences rang from 9% to 12.
Our study based on three species of Alviniconcha aims to (1) describe the current distribution of A. kojimai, A. boucheti, and A. strummeri in the western Pacific back-arc basins,  (2)  estimate their genetic divergence on different genomic datasets, mtDNA, nuDNA, and transcribed sequences (i. e. , transcriptomes) to test whether these divergences are congruent and proportional to the time elapsed since estimated species separation, (3) understand the demographic history of these species and whether they have diverged in allopatry, and (4) investigate whether these species display fixed morphological differences over their overall species range (i. e. , five western Pacific back-arc basins) using morphological traits previously highlighted as diagnostic in the volcanic arc of Wallis and Futuna  [28].
Upon recovery on board, samples were temporarily stored in tanks containing refrigerated sea water (4 • C) before being examined and dissected. Several soft tissues (gill, foot, mantle) were preserved in both 80% ethanol and RNALater, and fresh foot tissue was also used for immediate DNA extraction on board (see below). Geographic information about the gastropod collection used during this study is summarised in Table  S1. Fatu-Kapa (FK); Fati-Ufu (FU). Lau Basin: Mangatolo (Ma); Tow Cam (TC); ABE (ABE); Tu'i Malila (TM). Colours indicate species occurrence: purple, A. boucheti; turquoise, A. kojimai; yellow, A. strummeri. The map background was obtained using the R package marmap  [31]. Ten frozen (-80 • C) individuals from the TN 235 Lau expedition (2009) on board the R/V Thomas G. Thompson (chief scientist: C.
5 µL of diluted template DNA, 0. 1 µM of each primer, 50 µM of each dNTP, 2 mM MgCl 2 , 0. 5 U of Flexi GoTaq ® polymerase (Promega), and 0. 1 mg/mL bovine serum albumin in 1× Green GoTaq ® Reaction Buffer (Promega). The amplification protocol was as follows: 94  C for 1 min, and a final elongation step at 72 • C for 5 min, in a T100 thermocycler (Biorad). Because of the co-amplification of an additional nonspecific small fragment for some specimens with the forward primer, PCR products were sequenced only from the HCO2198 primer, using the Sanger method (Eurofins Genomics, Ebersberg, Germany). The Cox1 gene sequences were manually checked for polymorphic sites using CodonCode aligner (v. 5. 1. 5, Codon Code Corporation), edited in Bioedit  [38] , and aligned by ClustalX  [39]. One reference sequence representing each of the described Alviniconcha species from  Johnson et al.
[13]  and Suzuki et al. [40]  (Genbank accession numbers: AB235216; KF467675; KF467921; KF467741; KF467873; KF467896) was added to the dataset. Sequences of five individuals of the species Ifremeria nautilei also collected during the Chubacarc cruise were used as an outgroup. Haplotype and nucleotide diversities (Hd and π, respectively), the substitution rates at non-synonymous (d N ) and synonymous sites (d S ), and the absolute population divergence (d XY ) were estimated with DnaSP 6. 0  [41, 42]  between the three pairs of Alviniconcha species. McDonald-Kreitman tests were used to evaluate the degree of adaptive evolution of the Cox1 gene within and between species. Inter-specific pairwise F ST was calculated from haplotype frequencies with ARLEQUIN V. 3. 5  [43]. A haplotype network was created with the median-joining method  [44]  using the software PopArt  [45]  to depict phylogenetic relationships on the basis of the mitochondrial sequences.
Bioinformatic Filtering of Illumina Reads Raw reads were first demultiplexed using the process_radtags module of the Stacks software version 2. 52  [49, 50]  that also removed adapters and low-quality reads. Average sequence quality per read and GC content were checked using multiQC version 1. 7  [51]. Paired-end 144 base reads were assembled using the de novo pipeline in Stacks v2. 52  [49, 50]. Assembly parameters (m: 6; M: 14; n: 14) were chosen after empirical testing over a range of values (m: 2-6; M: 2-12; n: 2-18) on a subset of 23 individuals that included the three Alviniconcha species. Among these 23 individuals, 13 were triplicated for a total of 49 samples, which made it possible to evaluate the genotyping error rate according to the parameters tested. The applied parameter settings resulted in the greatest number of loci retained while maintaining a minimum genotyping error rate (see details in Supplementary Materials Figure  S2 ).
52, using a popmap of 570 individuals and the parameters defined previously  [49, 50]. First, a sub-popmap consisting of 150 individuals distributed among the three species (one species constituting one population) and the different basins sampled was used for the construction of the inter-individual catalogue (ctacks). Second, all individuals were mapped to this catalogue (sstacks). The raw SNP data were filtered against the following thresholds: minimum individuals sharing a locus in a population r ≥ 0. 8 and minor allele count MAC ≥ 4. Using this first dataset (hereafter referred to as the "raw" dataset), we examined the number of RAD loci and SNPs that were genotyped within each species or were shared between species, to get a picture of the impact of divergence between species in the RAD-seq dataset.
The SNPs identified by Stacks were then further filtered for missing data using R scripts to only keep the SNPs genotyped in 90% of the individuals and to keep only the individuals that were genotyped at more than 85% of the SNPs. In the end, a high-quality dataset of 60,084 SNPs for 498 individuals (250 A. kojimai, 212 A. boucheti and 36 A. strummeri) was obtained.
Population Structure, Divergence, and Admixture The population structure associated with the SNPs shared between the three Alviniconcha species was visualised in R using a principal component analysis (PCA; adegenet package  [52] ). The population divergence (d XY ) between species, the nucleotide diversity (π), and the net divergence (d A = d XY -(π X + π Y )/2) were estimated following Nei and Li  [42]  using the fstat option in Stacks-2. 52. F ST between species and between basins within species was calculated with the R package Hierfstat  [53]. Individual admixture coefficients were estimated using the function snmf from the R package LEA  [54]. Assuming K ancestral populations, the R function snmf provides least-square estimates of ancestry proportions for all analysed individuals [55].