dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS Special thanks to the  Tara Ocean Foundation , the  R/V Tara  crew and the  Tara Pacific Expedition Participants 110. We are keen to thank the commitment of the people and the following institutions for their financial and scientific support that made Tara Pacific expedition possible: CNRS, PSL,  CSM ,  EPHE, Genoscope/CEA ,  Inserm ,  Université Côte d'Azur, ANR , and the  Tara Ocean Foundation 's teams, crew,  Président Etienne Bourgois  and its partners agnès b. ,  UNESCO-IOC , the  Veolia Environment Foundation ,  Région Bretagne, Billerudkorsnas ,  Amerisource Bergen Company, Lorient Agglomeration, Smilewave, Oceans by Disney , the  Prince Albert II de Monaco Foundation ,  L'Oréal, Biotherm ,  France Collectivités ,  Fonds Français pour l'Environnement Mondial (FFEM) , the  Ministère des Affaires Européennes et Etrangères , the  Museum National d'Histoire Naturelle ,.
Tara Pacific  would not exist without the continuous support of the participating institutes. The authors also particularly thank  Serge Planes ,  Denis Allemand  and the  Tara Pacific consortium. This is publication number 25 of the  Tara Pacific Consortium. The acknowledgments to local people and authorities who made this study possible can be accessed in the associated Zenodo deposition 75. We would like to further thank  Hans-Joachim Ruscheweyh  for lending us computing time at  ETH  for some of the BFD* replicate runs. Work from  BP , PF, DF was supported by the  French Government (National Research Agency, ANR)  through the grant " Coralgene "  ANR-17-CE02-0020  as well as the  "Investments for the Future" programs LABEX SIGNALIFE   ANR-11-LABX-0028  and  IDEX  UCAJedi  ANR-15-IDEX-0001.
Work from BCCH was supported by  AFF  funding from the  University of Konstanz, Germany. We would like to thank the  SequAna Core Facility  at the  University of Konstanz  for providing computing time. Work from EJA was supported by the  French government (National Research Agency, ANR)  through the grant " France Génomique "  ANR-10-INBS-09. Work from GM is supported by  JST SPRING , Grant Number  JPMJSP2124. EB was supported by a  Marie Curie  fellowship  MC-CIG-618480  to study the biogeography of  Indo-Pacific  fire corals.
Due to escaped individuals, transferring errors or deaths, 25 trials had to be excluded from the dataset, resulting in 203 remaining mating trials. To test if the density treatment had a significant effect on the number of mating opportunities, we counted the number of contacts that the focal individual had with potential mating partners. We defined 'contacts' as any instance where the focal individual had contact with another individual in the vicinity of its head, ensuring that the focal perceived the other individual. Next, we recorded the mating behaviour of the focal individual of each trial. Specifically, we quantified the number of copulations, the number of different mates, and the duration of each copulation. A copulation was defined as a behaviour in which the male climbs onto the back of a female, with both bodies in the same orientation.
A successful copulation was counted as such if it lasted at least 30 s whereas a copulation attempt was counted as such from a duration of 5 to 29 s. This threshold was based on the observation that previously unmated females produced offspring after being mounted for at least 30 s, indicating that sperm transfer occurred within that time. Mounting events of a duration longer than 40 s have, however, been used as thresholds in previous studies  (Attia & Tregenza, 2004; Bloch Qazi et al. , 1996). Behavioural observations were done using the program 'PotPlayer' (Kakao). After the mating trials, males were preserved frozen at -30°C, whereas females were placed individually in vials containing 7 g of the flour mixture covered with 1 g of organic rolled oats (Alnatura), where they were left to oviposit for 1 week.
Afterwards, females were removed and preserved frozen at -30°C, while the vials containing their eggs were left in the incubator until all offspring had fully developed into adult beetles. Offspring numbers (wildtype or Rd) were determined 5-6 weeks after the females had been removed. The total number of adult wildtype offspring constitutes our fitness proxy of focal individuals. Whenever we found an offspring of a female, which we did not observe to copulate (14 cases; 1. 1% of females), we added a single copulation to the behavioural data of that female. All frozen focal females and males were weighed to the nearest 0. 01 mg on a Sartorius R200D balance within 6 months after the main experiment. After measuring a subset of individuals twice, we found the resulting body mass measurements to be highly repeatable (Intra-class correlation coefficient: ICC males = 0.
| Quantifying sexual selection We used complementary metrics of sexual selection (Table  1 ) to estimate and to compare the strength of sexual selection across different densities and sexes using data on reproductive and mating success. In particular, we quantified three standardized selection metrics derived from Bateman's principle, i. e. the opportunity for selection (I), the opportunity for sexual selection (I s ) and the sexual selection gradient (also called Bateman gradient; β ss ). Moreover, we computed the Jones index  (Jones, 2009)  providing a more complete proxy for the strength of pre-copulatory sexual selection  (Henshaw et al. , 2016). In addition, we quantified selection differentials and mating differentials for body mass to offer a more trait-based perspective on sexual selection.
Considering that data were not normally distributed, quasi-Poisson models were used, with the exception of quasibinomial models for the copulations per attempt and copulations per partner and Gaussian models for copulation duration. For direct comparison of the treatment groups, ANOVA results are being reported (type 3 ANOVA if interactions were included, otherwise, type 2 ANOVA). We initially included block and incubator as random terms, but as none of them improved the model fit (tested via a likelihood-ratio test), they were dropped. Each model initially included an interaction between group size and arena size treatment, but none were found to be statistically significant (Tables  S1  and S2 ), and we report analyses without the interaction term included in the model (Table  S3 ). Second, we computed standardized metrics of (sexual) selection and tested for differences among density treatments and sexes, including the standardized mating and selection differential on body mass (m' and s', respectively), a trait that is likely under sexual and natural selection.
All presented p-values were corrected for multiple testing within treatment and sex by using the 'false discovery rate' method (FDR;  Benjamini & Hochberg, 1995). We analysed all data including individuals that obtained no mating success. We consider these data meaningful for estimating the strength of sexual selection, as the absence of mating success (and with that of reproductive success) can be the manifestation of particularly strong selection and hence also increase I and I s  (Aronsen et al. , 2013; Wade & Shuster, 2005). Nevertheless, the first successful mating of an individual will always increase its reproductive success in outcrossing species, therefore Bateman gradients are very sensitive to their inclusion. Hence, we also report β ss excluding individuals without mating success, as a measure of the fitness benefit of multiple mating (Table  S5  and Figure  S7 ).
Similarly, the standardized selection differential (s') on female body mass was significantly different from zero in large but not in small groups, but there was no significant difference in s' between the group size treatments (Table  2  and Figure  1f ). Arena size had no statistically significant effect on all selection metrics in both sexes (Table  3  and Figure  1 ), with the exception that the male β ss was found to be steeper in smaller arenas (i. e. increasing density; Figure  1c ). Female and male β ss were positive under both arena sizes for the full dataset (Table  S4  and Figure  S6 ), but when individuals without mating success were excluded, only female β ss in the large arenas and male β ss in small arenas remained significantly different from zero (Table  S5  and Figure  S7 ).
The deformation around the defect is performed locally with a radius of 4. 0 Å and partial Hessian is projected on a 15-dimensional Lanczos basis  37. Exploring deeper into the energy landscape requires a method that can efficiently manage many search routines in parallel. The TAMMBER  41  code is a massively parallel sampling scheme that constantly deploys thousands of molecular dynamics workers, allowing a wide exploration of defect energy landscapes. When new minima are found, double-ended saddle search routines are used to find connecting saddle points to the existing network, whilst Bayesian analysis of the molecular dynamics data yields a robust measure of sampling completeness for each minima, namely an estimated rate to as-yet undiscovered regions of the energy landscape, represented by an absorbing sink.
Acknowledgements This work was financially supported by the  Cross-Disciplinary Program on Numerical Simulation of CEA , the  Cross-cutting basic research Program of the Energy Division of CEA (RTA Program) , the  RMATE  project of  CEA , the  French Alternative Energies and Atomic Energy Commission , and the  NEEDS program  ( CNRS-CEA-EDF-ANDRA-AREVA-IRSN-BRGM ). A. M. G. and M. C. M. acknowledge the support from  GENCI -(CINES/CCRT ) computer centre under Grant No. A0130906973. C. D. acknowledges the support from  GENCI -CINES computer center  under Grant No. A0050910624. C. D. and M. C. M. acknowledge  PRACE support from MARCONI-KNL at CINECA  under Grant No  2016153636. A. C. acknowledges the access to the HPC resources of the TGCC computing centre, under the DARI allocation A0100911528.
T. D. S. gratefully recognizes support from the  Agence Nationale de Recherche (ANR) , via the  MEMOPAS  project  ANR-19-CE46-0006-1 , and IDRIS resources under DARI allocation  A0090910965. This work has been carried out within the framework of the  EUROfusion  Consortium, funded by the  European Union  via the  Euratom Research and Training Programme  ( EUROfusion Grant  No. 101052200 ). The views and opinions expressed herein do not necessarily reflect those of the  European Commission. M. C. M. and A. M. G. gratefully thank  Dr. Emmanuel Clouet  for many insightful private conversations concerning dislocation interactions; and  Dr. Maylise Nastar  for the interesting discussions regarding the formation of precipitates. The electron irradiation experiments were supported by the  French EMIR&A accelerators network.
Effective metabarcoding gene regions (markers) require an appropriate level of sequence variability to distinguish among species/taxa of interest, and the primers that flank these diagnostic sequences must anneal to conserved sequences on either end  [31] [32] [33] [34] [35] [36]. When the appropriate gene region/marker and primers are selected, the internal sequence region contains nucleotide variants that can be used to distinguish among the species/taxa in a sample in comparison to reference sequence databases  [28] [29] [30] [31] [32] [33] [34] [35] [36]. Researchers thus aim to select the metabarcoding markers and corresponding primers that are most likely to identify the target taxa/species to the desired taxonomic level  [13, [28] [29] [30] [31] [32] [33] [34] [35] [36]. However, some markers are limited to higher systematic levels because they lack distinctive sequence variation (i.
For all MiSeq runs, we used DNA concentrations of 10pM, measured with a Qubit ® fluorometer. A 30% PhiX DNA spike-in was used to enhance data quality of samples that may have had low nucleotide diversities  [31, 36, 61, 62]. Illumina MiSeq runs were performed at the Ohio State University's Molecular and Cellular Imaging Facility (https://mcic. osu. edu/home), and the autumn 2018 samples additionally were run at the Oregon State University's Center for Quantitative Life Sciences (https://cgrb. oregonstate. edu/bioinformatics) to evaluate congruence. The different markers were run on separate lanes.
Bioinformatic processing Adapters and amplicon primers were trimmed from the sequence reads using the program Cutadapt (v 3. 7  [82] ), without length trimming and with each sequence pair requiring a match to either the forward or reverse primer (respectively). Adapter-trimmed sequence reads then were quality-filtered with DADA2 (v 1. 18. 0  [83] ), using minimum length of 100 bp, PhiX removal, limited quality truncation (trunQ=2), and maxEE(5) settings. High quality reads then were run through the DADA2 pipeline to evaluate errors, dereplicate reads, merge read pairs (having a minimum overlap of 20 bp), check for chimeras, and create Amplicon Sequence Variant (ASV) files for ASVs having two or more reads. To identify the taxon for each ASV, BLASTn  [84]  was queried against the NCBI (National Center for Biotechnology Information) nucleotide (nt) database [85] (downloaded 11/12/2021), whose output displayed the best "hit" (i.
e. , sequence match) for up to 4,000 target sequences, to circumvent individual taxa that had large numbers of sequenced individuals from dominating the BLASTn results. A negative taxonomic identification (taxID) list excluded taxIDs for which the "scientific name" category included "uncultured", "environmental samples", "metagenome", "unidentified", or "unclassified" labels, including nodes downstream of those identified using the program Subtree  [86]. BLASTn data then were reformatted to exclude matches less than the designated length cutoffs (300 bp for LrCOI, 100 bp for Cop16S and Mol16S, 140 bp for MiFish, 120 bp for FishCytb). Only taxIDs with the highest percent identity per ASV were included in taxonomic analyses. All taxIDs from the BLASTn results were converted to a standard taxonomic hierarchy using Taxonkit  [87] , which was modified further to fill in gaps in the taxonomic hierarchy by assigning missing values from the next lower hierarchical level that was identified (e.
Marker and morphology data comparisons were made by first merging identical taxonomic assignments across all comparison datasets. Venn diagrams were created in R  [91]. Results from our metabarcoding assays were compared with morphological identifications made from the same (split) sample. Species appearing unique to either the metabarcoding assays and/or morphology were evaluated from these samples and across the sampling locations. The nearest taxonomic levels to which each marker and morphological methods identified the species/taxa were evaluated using network plots using the visNetwork package in R  [92]. Biomass proportions of species from morphological identifications and the proportions of sequence reads for single metabarcoding assays were arcsine square root transformed and statistically compared using simple linear regression models and Pearson correlations in R, with probability (p-) values corrected for multiple comparisons and controlled for false discovery rate  [93].
Samples for which either the morphological or metabarcoding marker proportions were zero were excluded from analyses. Assignments of putative nonindigenous species were evaluated in further detail, since their indication might invoke management response (see  [9, 94] ). Their ASVs were examined for two factors: 1) percent identity (PID) to the assigned species/taxon, and 2) whether potential locallyoccurring species, or known nonindigenous species from the Salish Sea or other NE Pacific regions, had published sequences for that gene region. Additionally, these ASVs were assessed for: 3) number of sequence reads, 4) number of samples in which the ASV was found, and 5) the PID of next closest assignment. ASVs having low PID and/or lack of reference data for locally-occurring species were considered "low confidence" matches here.
Results PCR amplifications were not found in our negative extractions, centrifugations, no-template PCRs, indexing, or clean-up controls. The two technical replicates run for spring 2018 samples yielded very similar and congruent results. Duplicate DNA extractions and PCR library preps showed similar results. Supplementary Table  2  provides the total numbers of sequence reads (paired and single reads post-merge) at each step in the quality control pipeline. Amplicon sequence variant (ASV) counts for each metabarcoding marker are in Suppmentary Table  3. These results show that the ASV identifications had at least a median value of 99. 4% similarity to the reference sequence database for the markers, rangint from 99. 7% for Cop16S to 99. 4 for the other four markers. ASV identifications at the genus level ranged from a median of 94.
Species detected by morphology and missed by metabarcoding A few taxa were abundant in our morphology dataset but were not identified with metabarcoding, often due to poor representation in the NCBI reference database  [85]. Notably, cyclopoid copepods were missed by metabarcoding, including the abundant species Ditrichocorycaeus anglicus (up to 15. 3% in biomass). This species was well-represented in NCBI, and the LrCOI forward primer contained just one mismatch, but was not detected with any of the metabarcoding markers; this discrepancy is being further investigated in follow-up work. Cyclopoid copepods in the genus Oithona also were abundant in the morphology counts (up to 9. 43% of the individuals), leading to the expectation that they should be detectable with metabarcoding. However, their small individual sizes led to their proportionally small biomass representation (average 0.
52±0. 34. Their range [109] corresponded to the correlation values we obtained for copepods. We similarly found that our results were quite variable among different taxa using LrCOI, for which three crustaceans (of 12) had significant corrected p-values (Calanus pacificus, Metridia pacifica, and Euphausia pacifica). Lamb et al. (2019)  [109] discerned that weaker relationships occurred when species varied greatly in body sizes. In our results, correlation between relative abundance and biomass increased when analyses were restricted to just the Copepoda as opposed to using the entire dataset, suggesting that including taxa having vastly different biomass can skew the results. In our results, the species with the greatest ranges in biomass showed significant correlations. This finding makes sense given an average four-fold potential shift in relative abundance predicted from PCR bias alone in models by  Shelton et al.
Non-indigenous species detections The present investigation identified possible nonindigenous species, whose larval distributions can be assessed with metabarcoding. We evaluated the quality of the taxonomic assignment to each of these species (Methods and Supplementary Table  6 ), since invasions can have implications for management responses  [9, 93]. We assessed nine putative nonindigenous marine/estuarine species, four regarded to have high confidence due to percent identity matches to the sequence reference database and our other criteria (Supplementary Table  6 ). The remainder are excluded from discussion here due to lower percent identity matches and/or missing reference sequences for potential closelyrelated local species (see Supplementary Table  6 ). Notably, high confidence values support two nonindigenous polychaetes found by the LrCOI marker: Proceraea okadai and Hediste diadroma, which both have known introductions to the Salish Sea (and San Francisco Bay) from Asia  [98] [99] [100].
Species identifications and reference libraries Correct species identifications are vitally important for valid ecological comparisons, yet often are overlooked. Notably,  Bianchi and Gonçalves (2021)  [138] pointed out that misidentifications and problematic taxonomic metadata are a recurrent issue significantly affecting sequence reference databases, including Global Biodiversity Information Facility (GBIF), the Barcode of Life Data System (BOLD), and NCBI GenBank. Misspellings and invalid names sometimes exceed 10% of deposited sequences for a given species, along with poor-quality sequences  [139]. Such mistakes compromise the integrity of reference databases and appear to affect many studies (see [9,  43, 52, 112, [138] [139] [140] [141] [142] [143] [144] [145] ). Here we found limitations due to the numbers of species that lacked diagnostic sequence data in reference databases at the time of our study (see Figs  6A  and F ).
In the future, metabarcoding data sets such as ours can be used to identify species from the original sequences that were not diagnosable at the time, by querying improved and augmented reference sequence databases. Additionally, longer sequence reads will allow greater accuracy in identifications (see  [37] ), which are currently hampered by the limitations in sequence read lengths with most existing HTS platforms  [140] [141] [142] [143]. As shown by our investigation, the use of multiple gene regions and multiple markers can significantly enhance taxonomic coverage, confidence, and reliability for present-day metabarcoding efforts.
Ecological patterns and trends Species that are congruently identified by more than one metabarcoding marker and gene region provide strong verification of their presence in field-collected samples  [42, 52]. For rare and poorly known taxa, especially at their early life stages, metabarcoding identifications are very valuableprovided that the sequence reference databases exist and are correct  [138] [139] [140] [141] [142] [143] [144] [145]. The larval macroinvertebrates and fishes assayed in this project are the key to adult recruitment for the abundant fisheries of the Salish Sea and much of the NE Pacific coast. It is these early life history stages that are under the most intense selection, and are the most ephemeral and vulnerable (see  [146] ). For example, it is hypothesized that warming and other anthropogenic factors may alter the timing of phytoplankton blooms, and their reliant zooplankton populations; this may put larval communities out of synchrony with their prey  [12, 13, 16].
Conclusions This multi-gene metabarcoding marker approach holds considerable promise to advance scientific ability for assessing species identities and compositions of planktonic communities, as well as evaluating their responses to environmental conditions. Moreover, the approach is powerful and data-rich, yielding results that are readily integrated into existing ecological sampling and modeling efforts. Currently, there are species identification issues stemming from lack of available reference sequences, along with museum voucher specimens, preserved tissues, and DNA samples for comparisons; sequence databases also are fraught with incorrect erroneous scientific names and incorrect systematic classifications [9,  43, 52, 112, [136] [137] [138] [139] [140] [141] [142] [143] [144] [145].
Data Availability Statement: Sequencing read data are available publicly through the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), under project accession PRJNA915814. Supplementary materials (figures, tables, and datasets) for the morphological and metabarcoding marker data and results, including analysis products, are archived on figshare at https://figshare. com/projects/Supplementary_Material_for_Evaluating_mtDNA_metabarcoding_markers_to_i dentify_zooplankton_and_ichthyoplankton_in_the_Salish_Sea_Resolution_in_comparison_to_morphology_/1 56479. The custom script used for taxonomic identification by SMM is accessible on GitHub at https://github. com/McAllister-NOAA/asv_to_taxonomy.