dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING This study was supported by and is part of the following research projects: remOcean ( ERC grant  agreement  246777 ),  NAOS  ( Agence Nationale de la Recherche , " Equipement d'avenir" program , grant agreement  ANR   J11R107-F ), EQUIPEX  NAOS (Novel Argo Observing System)  project  [French National Research Agency (ANR) , reference  ANR-10-EQPX-40 ],  AtlantOS  ( EU   Horizon 2020 Research and Innovation Program , grant agreement  2014-633211 ),  SOCLIM (Fondation BNP Paribas) , the  French Bio-Argo program (Bio-Argo France ; funded by  CNES-TOSCA ,  LEFE Cyber , and GMMC), and  NSF's Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM)  project under the  NSF  Award  PLR-1425989 , with additional support from  NOAA  and  NASA  (award  NNX14AP49G  and  NNX14AP496 ).
Several international organizations, the most active being International GNSS Service (IGS) and European Center for Medium-Range Weather Forecasts (ECMWF) pool core GNSS stations around the world, essentially to provide data to maintain the International Terrestrial Reference frame (ITRF) and the Vienna mapping function 1 (VMF1) family of mapping functions (used to correct tropospheric delays). These stations are unevenly distributed around the world, with a repartition driven by the density of populations. Tahiti Island is an extreme case, with the THTI and FAA1 stations located 2. 56 km apart and surrounded by nearly unlimited seas. The nearest IGS station is located on the Mangareva Island, at a distance of 1500 km from Papeete. As a result, these isolated stations have a heavy weight on worldwide networks, as they constrain geophysical models in a large radius.
As we have the chance in Tahiti to have two nearly collocated but also isolated stations, it then makes sense to study the differences in their time series, in order to assess the errors that can be induced in global models by the data from such isolated stations. Geomorphological and climate settings are described in Section 2. In Section 3, GPS settings of the THTI and FAA1 IGS stations are given. In Section 4, the estimation of GPS PW is presented in detail. The exponential distribution of PW with altitude is given in Section 5. The results and discussion are presented in Section 6, which includes the results of zenith total delay (ZTD), zenith non-hydrostatic (wet) delay (ZWD), PW, and their statistical analysis; the comparison between GPS PW and the PW values based on an exponential distribution with respect to attitude; and the correlation between ZWD, PW, and wind velocity, and the influence of local topography.
The climate in Tahiti is very humid (meaning high PW values)  [20]  and characterized by dry (from May to October) and wet seasons (from November to April) that are governed by the South Pacific Convergence Zone (SPCZ) on the large scale and by the topography of a high volcanic island on the orographic scale  [21]. The two GPS stations of this study are known in the IGS database as THTI and FAA1. Their altitude difference is 86. 14 m, and their horizontal distance difference is only 2. 56 km. Although they are close (Figure  1 ), their surroundings are quite different. FAA1, managed by Météo-France, is located at near sea-level, under the premises of the international airport, on a very large area that was flattened to host the airstrip. THTI is located on the main campus of University of French Polynesia, a little bit more inland, on a ridge crest with a slope of about 8%, at a distance from the sea of about 1.
GPS Settings of the THTI and FAA1 IGS Stations The THTI station consists of a SEPT POLARX5TR receiver with a LEIAR. R425. R3 antenna, and the FAA1 station consists of a SEPT POLARX4 receiver with a LEIAR. R4 antenna. GPS satellite precise orbits and clocks as well as consistent Earth-rotation parameters were provided by the Center for Orbit Determination in Europe (CODE). Together with the Bernese GNSS Software Version 5. 2 with PPP approach, hydrostatic and wet Vienna Mapping Function 1 (VMF1) were used to estimate the THTI and FAA1 ZTD estimates with a resolution of one hour  [23] [24] [25]. To validate the accuracy of our ZTD estimates, we made a comparison with the IGS Final troposphere products (ftp://cddis. gsfc. nasa. gov). Details of both data processing strategies (ours and IGS) are summarized in Table  1.
ZTD Estimates for THTI and FAA1 In this section, we give the comparisons of THTI ZTD and FAA1 ZTD estimates based on our GPS data processing over three years (May 2016 to April 2019), with respect to the IGS Final tropospheric products. The temporal resolution is one hour. As displayed in Figure  2a ,c, it can be noticed that the GPS-derived ZTD from our GPS data processing agrees very well with the corresponding ZTD from IGS Final products, for both stations (except from 9 July to 13 August 2018 with no GPS data measurements). Figure  2b, d  and Table  3  show the distributions of their differences, after removing off the outliers (±3 sigma). They are close to Gaussian distributions. The ZTD differences are probably mainly due to the details of the algorithms and strategy used, with no significant systematic biases.
The root mean square (RMS) values of ZTD differences are 5. 37 mm at the THTI site and 8. 21 mm at the FAA1 site. FAA1 is located near sea-level, and this may be the cause of its slightly lower accuracy with respect to the IGS Final products. γ = a (° ) (° ) + ln(/100),  (13)  where K is the Boltzmann constant and RH is the relative humidity,  is the dew point temperature in degrees Celsius (°), a = 17. 271 and b = 237. 7. (°) is the surface temperature in °  [17]. Observationally, the scale height of water vapor (H) in the Earth's atmosphere is between 1. 5 and 2 km  [18]. Butler (1998)   [16]  assumed the scale height of water vapor to be 1. 5 km. Vicente and Pulido (2012)  [17]  investigated the annual average scale (1340 ± 120 m) from data obtained by two balloons, which provided data for pressure, height, temperature, dew point, relative humidity, and other parameters along their upwards trajectories through the atmosphere from 630 m to 30 km.
2 are inputting ECMWF meteorological data (see following Section 6. 2). (a) (b)  Figure  3  shows the inter-comparisons of our ZTD estimates (Figure  3a ) and IGS ZTD estimates (Figure  3c ) between THTI and FAA1 at the same epoch, and Table  4  summarizes the results. It can be noticed that there is a larger bias (around 30 mm) between our THTI ZTD and FAA1 ZTD estimates, also seen between the IGS THTI ZTD and IGS FAA1 ZTD estimates. The difference in altitude between the two GPS stations may play a role here, with different meteorological parameters entering the mapping functions used in the respective processing. It must be emphasized that the VMF1 mapping functions (Table  1 ) used in the Bernese 5. 2 are inputting ECMWF meteorological data (see following Section 6. 2). Figure  3  shows the inter-comparisons of our ZTD estimates (Figure  3a ) and IGS ZTD estimates (Figure  3c ) between THTI and FAA1 at the same epoch, and Table  4  summarizes the results.
It can be noticed that there is a larger bias (around 30 mm) between our THTI ZTD and FAA1 ZTD estimates, also seen between the IGS THTI ZTD and IGS FAA1 ZTD estimates. The difference in altitude between the two GPS stations may play a role here, with different meteorological parameters entering the mapping functions used in the respective processing. It must be emphasized that the VMF1 mapping functions (Table  1 ) used in the Bernese 5. 2 are inputting ECMWF meteorological data (see following Section 6. 2).
The ZHDs as derived from the ECMWF data are in error by about 1. 32 mm, and the RMS is about 1. 99 mm compared to the local pressure values. Therefore, the ECMWF meteorological data meet the requirement of the level of accuracy  [23]  and can be used for our following data processing. the ECMWF meteorological data meet the requirement of the level of accuracy  [23]  and can be used for our following data processing. Figure  5a ,c shows the time series of pressure and temperature for the THTI and FAA1 sites, as extracted from the ECMWF database. The temporal resolution is six hours. Their differences are shown in Figure  5b ,d and summarized in Table  6. The standard pressure difference between THTI and FAA1 coming from the difference in altitude should be -10. 30 hPa (green dots in Figure  5b ), instead of the -9.
81 hPa (Table  6 ) value from the ECMWF database, according to the standard  Figure  5a ,c shows the time series of pressure and temperature for the THTI and FAA1 sites, as extracted from the ECMWF database. The temporal resolution is six hours. Their differences are shown in Figure  5b ,d and summarized in Table  6. The standard pressure difference between THTI and FAA1 coming from the difference in altitude should be -10. 30 hPa (green dots in Figure  5b ), instead of the -9. 81 hPa (Table  6 ) value from the ECMWF database, according to the standard formula P = 1013. 25•(1 -0. 0000226•H) 5. 225   [23, 49]. This is coherent with the bias in pressure between local THTI and ECMWF THTI data (0. 58 hPa) (Table  5 ). The difference in temperature between THTI and FAA1 should be -0. 56 K (green dots in Figure  5d ) according to the standard lapse rate of 0.
Comparisons between GPS PW and PWE for THTI and FAA1 In this section, we analyze the differences of PW values between THTI and FAA1 based on exponentially derived PW estimates (PWE, Equation (  15 )) to see if the differences in the PW time series can be attributed to the difference in altitude. For the calculation of PWE (THTI) and PWE (FAA1), the dew point temperature or the relative humidity and the surface temperature are needed (Equation (  11 )). The FAA1 site has dew point temperature records, which can be retrieved from the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface Database (ISD) (https://www. ncdc. noaa. gov/isd). ISD includes numerous parameters such as wind speed and direction, wind gust, temperature, dew point, cloud data, sea level pressure, altimeter setting, station pressure, and various other elements as observed by each station  [53].
Correlation between ZWD, PW, and Wind Measurements In this section, we analyze the differences of ZWD and PW values between THTI and FAA1 during the period of 2017-2018 (we had at our disposal two full seasonal cycles) to see if the differences in these time series can be attributed to an effect induced by the wind. We used two wind datasets, the first one relative to a 10 m altitude, from a meteorological mast collocated with the FAA1 GNSS receiver (ftp://ftp. ncdc. noaa. gov/pub/data/noaa) and the other one relative to a 1000 m altitude, from the RS balloon station of the airport, also collocated with FAA1. Figure  13a ,c shows the variations of the 10 days averaged values of the wind velocity from NOAA and RS with the 10 days averaged values of ZWD values (Figure  13a ) and PW values (Figure  13c ).
Acknowledgments: We would like to thank the  Center for Orbit Determination in Europe (CODE)  for providing access to the GPS database, the  European Center for Medium-Range Weather Forecasts (ECMWF)  for giving access to the VMF1 data files, the  National Oceanic and Atmospheric Administration (NOAA)  for providing the surface temperature and the dew point temperature and the final  IGS (International GNSS Service) CDDIS (Crustal Dynamics Data Information System)  for giving final ZTD products. We would also like to thank  Benoit Stoll  for his help with Figure 1. We also thank the two anonymous reviewers that helped to clarify the scientific contents. We thank  Meteo-France  for providing all the data relative to their weather station located at the  International Airport of Tahiti.
The probability of counting the same shark more than once was reduced because of the simultaneous deployment of adjacent stereo-BRUVS and because sharks remained a long time around the same device. Individuals already recorded and identified through morphometric and markings were removed from the dataset. Shark total length and distance from camera were measured using the EventMeasure software package (www. seagis. com. au). Behavioral responses to bait. Two traits of shark feeding behavior were recorded from each video. The first trait was the bite on the bait. It was coded 0 in the absence of bite and 1 when a grey reef shark bites the bait during video recording. The second trait was the approach towards the bait. It was measured from the entrance of the first individual in the field of view until it gets closer than 1 m from the bait.
Shark occurrence and behavior models. To investigate how isolation from humans affects the occurrence and behavior of grey reef shark, we extracted the marginal effect of the travel time from the capital city on the probability of occurrence, the probability of biting the bait and the probability of each approach type from the GLM and multinomial models using the visreg package  32. To evaluate the efficiency of management, we used two approaches: (i) we compared the marginal effect of travel time in the different management areas providing the net benefit of the different regulations along the isolation gradient; (ii) we extracted the fitted values of the models in the different reserves and performed pairwise permutational Student t-tests to identify their effectiveness against two extreme benchmarks (i.
Models accuracy was evaluated using odds ratio, kappa coefficient and Fisher's exact test that assessed the dependence between the observed and predicted value and tested its significance. Then, the model prediction accuracy was evaluated using the confusion matrices, the sensitivity and the specificity. Each model showed odds ratio below to 1, kappa coefficient close to 1 and Fisher's exact test p-value below 3. 07 10 -6 , verifying a significant dependence between observations and predictions. Each model showed high sensitivity (ranging from 0. 55 to 0. 90) and specificity (ranging from 0. 65 to 0. 90), reducing the probability of erroneous predictions (Supplementary Table  S3 ). Pairwise comparisons between management categories were performed using permutational Student's t-test from the RVAideMemoire package  34.
Effect of the abundance of competitors and individual features on shark behavior. In addition to environmental and human-related variables, we evaluated the effect of the abundance of conspecifics and heterospecifics (i. e. other shark species) on the grey reef shark behavior. The foraging behavior of grey reef sharks is context dependent and may be modified according to the abundance of conspecifics in a given area with individuals exhibiting a bolder behavior when in groups  39, 40. The number of conspecifics and heterospecifics recorded on the videos varied from 0 to 12 and from 0 to 3, respectively, while the number of conspecifics and heterospecifics between the arrival of the first individual and the bite varied from 0 to 6 and from 0 to 3, respectively. To evaluate the effect of surrounding sharks on bite occurrence and approach type, we performed a permutational analysis of variance (PERMANOVA, 999 permutations, R statistical software) using the number of conspecifics and heterospecifics present in the video between the arrival of the first grey reef shark and the first bite.
64 to 1. 99 m (mean ± SD = 1. 15 ± 0. 29 m). Sex was determined for 97 individuals and the sex ratio was biased toward females (1:1. 5). To evaluate the effect of these individual features on bite occurrence and approach type, we performed a permutational analysis of variance (PERMANOVA, 999 permutations). To ensure an individual response, we used data where the first shark entering the field of view was the same individual to bite the bait first (80 individuals retained). These two explanatory variables could not be included in the full Generalized and Multinomial models since only measured on a limited number of individuals. We tested their effect apart to justify their exclusion from the final models.
Acknowledgements We are grateful to the  crews of research vessels Amborella (Government of New Caledonia) and Alis (IRD)  for the help in data collection during the oceanographic campaigns. We especially thank  Gérard Mou-Tham  and  Auriane Jones  for processing a large quantity of stereo footages. We would like to thank  Pierre Lopez  for the drawings of sharks used throughout the manuscript. We thank one anonymous reviewer and  Dr. Justin Rizzari  for valuable comments on the manuscript. This study is part of the PRISTINE and APEX projects funded by the TOTAL foundation and part of a PhD funded by University of New Caledonia. Oceanographic campaigns in protected areas were conducted with the permission of the South Province (decree no. 77-2014/ARR/DENV ),  North Province  (decree no. 60912-2163-2014/JJC ) and under the convention no.
Species of Microstomidae have been considered difficult to distinguish through traditional morphological methods. Their small size, relatively few morphological characters, and high amounts of intraspecific variation  [4, [9] [10] [11]  all present taxonomic challenges, especially as sexually mature specimens are generally rare. Furthermore, the taxonomic literature may sometimes lack detail and is often old and/or difficult to access, such that even the otherwisereliable WORMS database (http://www. marinespecies. org/aphia. php?p=taxdetails&id= 142210) includes numerous mistakes (e. g. recognition of M. hanatum, still listing M. giganteum as synonymous to M. lineare). Taken together, it is unsurprising that while there has been some recent studies of species diversity within Macrostomorpha in general (e.
The presence of multiple distinct but cryptic lineages in single locations potentially could have much larger implications for research on Microstomidae, especially since studies in the past have often been performed on specimens collected from environmental samples (e. g. [10] [11] [38] [39] ). In this paper, we reconstruct the phylogenetic relationships of Microstomum using both previously published and new 18S and CO1 nucleotide sequences. Our dataset comprises sequences from 336 specimens representing 15 previously described nominal species and eight new species. The new taxa are diagnosed on the basis of DNA-taxonomy as well as morphological characters that distinguish them from all other known Microstomum species. We also review the morphological taxonomy of the species within Microstomidae and provide a key to aid in future research and identification.
S2 Table lists primer pairs and protocols used for amplification and sequencing. Products were viewed on 1-2% agarose gels, purified using ExoSAP-IT enzymes (Exonuclease and Shrimp Alkaline Phosphatase; GE Healthcare) and sent to Macrogen Europe (Netherlands) for commercial sequencing. Amplification was performed on the complete nuclear 18S gene as well as a ~650 bp segment of the mitochondrial cytochrome oxidase c subunit 1 (CO1) loci pertaining to the "Folmer region"  [41]. The 18S ribosomal gene is currently the most commonly used molecular marker for flatworms on GenBank (8895 entries as of 5 July 2018), while CO1 gene sequences are known to be highly variable in Platyhelminthes (e. g. [4, [42] [43] [44] ) and other meiofauna (e. g. [45] [46] [47] [48] [49] [50] ) and are thus applicable in identifying species and assessing intraspecific diversity.
Two catenulid species, Stenostomum leucops and Parcatenula sp. , and one polyclad species, Hoploplana californica, were selected as outgroup taxa. Catenulida is considered the sister group of all Rhabditophora, and Polycladida is the closest relative of the Macrostomorpha  [51] [52]. Outgroup sequences as well as available species of Macrostomorpha were downloaded from GenBank and combined with new sequences for analysis. S1 Table  list  accession numbers for all specimens used in subsequent analyses Digital morphological vouchers (pictures and video) as well as collection data of four previously undescribed Microstomum species were downloaded from the Dryad Digital Repository (https://datadryad. org/resource/doi:10. 5061/dryad. b5908) and the Macrostomorpha Taxonomy and Phylogeny website (http://www.
macrostomorpha. info/). Further information on the collection and processing of these species can be found in Janssen et al. [7]. Sequence assembly and alignment was performed in Mega v 7. 0. 21 [53] , and trace files were manually edited. Alignments for each marker were generated using MUSCLE  [54]  with the default settings (see S1 File for the full-contatenated alignment). CO1 sequences were translated to amino acids using the flatworm mitochondrial genetic code  [43] , manually checked for stop codons and reading frame shifts, aligned and then reverted back to the original nucleotides for analysis. Maximum likelihood (ML) analysis was performed on each marker individually along with concatenated datasets in IQTree v1. 6. 4. The best fitting model was determined using the ModelFinder algorithm  [55]  implemented in IQTree.
We performed 1000 ultrafast bootstrap replicates  [56]  in IQTree, applying edge-proportional partitions with best-fit models independently selected for each gene in the concatenated datasets. Species of Microstomidae were inferred using the Bayesian implementation of the Poisson Tree Processor (bPTP) in the web-based interface (http://species. h-its. org/) with the default parameters  [32]. Input trees were generated from ML analyses of concatenated datasets that included Microstomidae sequences and two sequences each of Myozonaria bistylifera, Myozonaria fissipara and otherwise unidentified Myozonariinae downloaded from GenBank as outgroups (S1 Table ). All bPTP tests were re-run up to three times with the input tree pruned so that each prospective species included a maximum of five randomly selected sequences in order to ensure that results were not influenced by an imbalance in the number of specimens in any given clade.
Following recommended guidelines, outgroups were not included in any bPTP analysis. Since bPTP does not account for possible divergent rates of intraspecific variation that may occur from, e. g. , selection or population bottlenecks, we additionally implemented the multi-rate Poisson Tree Processor (mPTP) via its web-based interface (https:// mptp. h-its. org). mPTP is an improved model and implementation of Poisson tree processor that alleviates previous shortcomings by fitting the branching events of each delimited species to a distinct exponential distribution  [33]. The alignment used for the input tree as well as the ML input tree are given in S2 File and S3 File respectively. All DNA sequences are deposited in Genbank and their accession numbers are given in S1 Table. Alignments and trees are deposited in Dryad and may be accessed via https://doi.
Few adhesive papillae present on anterior and posterior ends of body. Rhabdites sparsely present throughout body. Nematocysts particularly concentrated at posterior end. Preoral intestine extending anterior to brain. Reproductive system unknown. GenBank accession number for partial CO1 sequence MK504554; for 18S MK504480. Etymology: Named for Lars Fredrik Afzelius, the first director of the Tja ¨rno ¨Marine Laboratory, in appreciation of his contributions to the marine scientific community. Material examined: Holotype, asexual specimen, Sweden, Sto ¨mstad, Salto ¨, 58˚52'29"N 110 8'41" E, 18 June 2016, 10 cm depth, marine, eulittoral sand, S. Atherton leg. (SMNH-Type-9151; GenBank Accession CO1 MK504554, 18S MK504480). Additional materials, asexual specimen, same as type (SMNH-Type-9152). Description: Microstomum with a total body length of 0.