dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The fieldwork in Canada trip has been funded by the  French ambassador (D. Marty-Dessus)  and  SFU. We gratefully acknowledge B. van  Wyk de Vries (LMV) , J. Clague,  G. W. Jones  ( SFU ),  K. Russell  ( UBC ) and  P. Friele , for fieldtrip assistance. Laboratory analyses have been funded by  Volcanology laboratory and CNRS  in LMV ( R. Paris  and  V. Cayol ). J. L. Devidal,  C. Constantin  and  J-M. Hennot  (LMV) are thanked for their expertise in making the thin sections and for SEM assistance,  J. Cellier  and  R. Thirouard  for XRD analyses (UCA-PARTNER-DRX in Clermont),  J. Clague  and  F. van Wyk de Vries  for editing the English writing. We acknowledge two anonymous reviewers and the Chief Editor  Dr. Jose Luis Macias  for the constructive comments and suggestions that have improved the manuscript.
, 2020; Hannah et al. , 2011; Ruhi, Messager, & Olden, 2018). A particular problem for hydrological variables is the "spatial footprint" of the area represented by an individual river gauge (in comparison to a point temperature or precipitation measurement) in determining time series homogeneity. This is reflected by the aforementioned difficulty of separating human influence from natural drivers of hydrological drought  (Van Loon et al. , 2016a , 2016b). For such reasons, endeavours such as large-scale hydrological data rescue (e. g. , Le  Gros et al. , 2015) , reconstructing long-term and large-scale F I G U R E 2 Spatio-temporal scales and associated dynamics characterising hydrological system variability high-resolution climate datasets  (Devers, Vidal, Lauvernet, Graff, & Vannier, 2020)  and corresponding near-natural hydrological datasets (e.
g. ,  Hanel et al. , 2018; Moravec, Markonis, Rakovec, Kumar, & Hanel, 2019)  are central in understanding the large temporal and spatial variations of hydrology. Compatibility between, or merging of, nationalscale datasets (e. g. ,  Caillouet, Vidal, Sauquet, Graff, & Soubeyroux, 2019; Keller et al. , 2015)  would be a further advance, as would improved quality assessment of large repositories such as the Global Runoff Data Centre under the auspices of the World Meteorological Organisation. Compared to hydrological data, climate observations are more widely available, or more easily and robustly simulated as in the case of reanalysis data. In part because of this disparity, climate data are sometimes substituted for hydrological data. A common example is the increasing use of meteorological indices such as the SPEI as proxies for hydrological drought (e.
3D ). Dynamic soaring birds can exploit more flight energy when they pass through stronger wind speed differences. As the wind speed difference is strong close to the sea surface, how close to the sea surface a bird can fly is crucial for dynamic soaring birds. Accordingly, long wings may restrict the minimum height at which the bird can fly and disturb efficient dynamic soaring. Hence, considering the effect of wings is crucial for evaluating dynamic soaring performances. Third, we assumed that the height of the animal was higher than 0. 5 m, that is,  ≥ 0. 5  [17]. The optimization problem described here is a restricted non-linear optimization problem. We used the SQP method to solve the problem with the 'fmincon' function in MATLAB ®︎ Ver R2019a. Quantification of the thermal soaring performance and the required upward wind speed For the computation of glide polars and circling envelopes, we followed the same procedure as the Flight software developed for evaluating bird flight performance, as described in  (23).
Acknowledgments The authors thank  Chihiro Kinoshita  for illustrating Fig. 1 and  Shin-ichi Fujiwara ,  Sophia Friesen , and  Michael Le Page  for reading the draft or preprint of this paper and for providing valuable comments. This study was financially supported by  Grants-in-Aid for Scientific Research  from the  Japan Society for the Promotion of Science  ( 16H06541 ,  16H01769 ), and  JST CREST  Grant Number  JPMJCR1685 ,  Japan. The authors also received financial support from  JSPS  as part of an  Overseas Research Fellowship program. Author contributions: YG, KY, HW, and KS designed the study; YG conducted the computation; all authors participated in the writing of the manuscript (YG wrote the first draft). Competing interests: The authors declare no competing interests.
The measurements at Taunus Observatory began earlier and continued longer, but here we focus on the concurrent sampling effort conducted within the framework of the EU FP7 BACCHUS project (Fig. 1 ). Due to the failure of the complementary aerosol instrumentation at the Caribbean site, sampling was interrupted between December 2015 and May 2016. Furthermore, the exchange of sampling substrates with the Amazonian site was logistically challenging. Thus, the Amazon data set is represented by several shorter periods of continuous measurements. Difficulties in obtaining sufficiently clean new substrates also prevented measurements below -25 • C between October 2015 and February 2016 in some cases. Typically, aerosol samples were collected with PEAC7 daily or once every two days at local noon. Sampling time was prescribed to 50 minutes with a 2 L min -1 flow rate.
Site specific INP characteristics At each measurement station a diverse array of supplementary meteorological, aerosol and gas data from the stations were collected in parallel to the INP sampling. Unfortunately, the parameters, instrumentation and time coverage vary considerably between the four sites. Observations include typical meteorological parameters such as temperature, relative humidity, precipitation, etc. , as well as the total aerosol particle number and mass concentrations, aerosol size distributions, black carbon concentrations, aerosol optical thickness, gaseous pollutant markers and greenhouse gases. However, despite a rigorous effort including correlation analysis, factor analysis and trajectory sector analysis, we were ultimately unable to identify a single parameter or a set of parameters that account for the total observed variation of INPs.
TO During the time frame of the global sampling effort (about 640 days) 400 PEAC7 samples were collected and analyzed from TO (i. e. 1 sample every 1. 6 days). The sampling frequency of valid INP concentrations (i. e. above the detection limit) remains as good as 1 sample every 2 days for measurements at -20 • C and -25 • C. This is by far the best data coverage of the four stations. We found a moderate but significant correlation between the PM 10 concentrations and INPs throughout the spectrum of T and RH conditions. The Pearson correlation coefficient is as high as R = 0. 28 (N = 293) at -25 • C, where we have the best data coverage. Although the particulate matter was significantly enhanced, when wind was coming from the heavily populated https://doi. org/10. 5194/acp-2020-667 Preprint.
Conclusions The data from our small but unique measurement network can be considered particularly valuable, and we hope lessons can be taken from this effort that will help to guide future INP monitoring efforts. Significant infrastructural and logistical investments are represented by the INP measurements that cover an observational period of 21 months in total. Well above 1000 samples were collected, retrieved and analyzed in this project at a large array of temperature and supersaturation conditions, characterizing the INP concentrations in the deposition and condensation freezing modes. The investigated sites represent diverse climatic regions and ecosystems that experience varying degrees of anthropogenic influence. In spite of the great differences in basically all characteristics that are expected to define the aerosol concentration, composi- Overall, we did not detect much evidence for a strong anthropogenic impact on the concentrations of ice nucleating particles.
Acknowledgements. The work was initiated and primarily carried out within the context of the EU FP7-ENV-2013  BACCHUS  project under Grant Agreement 603445. The experimental methods and instruments were established through financial support of  Deutsche Forschungsgemeinschaft  under Research Cooperations  SFB 641  and  FOR 1525  (INUIT). We thank the technical and scientific personnel of the stations ATTO,  Zeppelin Observatory  ( Norwegian Polar Institute ), and of the  Volcanological Observatory of Martinique (Frédéric Jadélus and David Mélézan)  for their valuable help on site during the campaigns. We thank  AWI  and  AWIPEV  staff, including  Verena Mohaupt , for the logistics provided in sample transport to and from  Ny-Ålesund. We thank  Hans-Christen Hansson  for his valuable scientific input and the supporting aerosol data from Ny-Ålesund.
Camera trapping was conducted for 30 successive days in both sessions (Table  2 ). A capture event was defined as all photographs of unique individuals within a 30min time period  (Di Bitetti et al. 2006; Farris et al. 2015). A sampling occasion was considered as one day (24 h)  (Otis et al. 1978; Wang and MacDonald 2009). Camera traps were inspected at least once every two weeks to check battery system charge and to download data from memory cards. At the end of each trapping period, the cameras were retrieved and the images downloaded. The trapping effort was obtained by multiplying the number of traps by the number of active capture days over the considered periods (Table  1 ). Capture per unit effort (camera trapping sampling occasion) was calculated by dividing the numbers of trapped cats per 100 trap-days.
Data analyses Camera trapping was used to calculate three complementary indicators of population abundance and density pre-and post-culling: (i) a general index of feral cat activity (GI), (ii) the minimum number of feral cats present in the study area (MKTBA), and (iii) feral cat absolute density (SECR). The general index (GI) allowed us to estimate feral cat activity over the study area by measuring the mean of virtual camera capture events per station and per sampling occasion. This index follows the equation of  Engeman (2005) : 1 1 1 1 sj d ij j i GI x d sj = = = ∑ ∑ , with d = the day, s = the station, and x ij the number of captures at the i th station on occasion j th. To compare the GI calculated before and after culling, we used bilateral mean comparison: t-test with Welch approximation for unequal variance.
Culled cats were identified using the same morphological criteria from the pictures of both flanks to (i) identify cats camera trapped during the pre-culling session and (ii) match right-and left-flank pictures of the same individual from the pre-culling pictures. The minimum number of feral cats known to be alive (MKTBA,  Lazenby et al. 2015)  was calculated as the total number of individuals identified from one side (left or right side of all cat pictures). This ensured the identification of a maximum of individual cats. Since uniformly black cats are very difficult to identify individually, we assumed that our number of different black individuals was an underestimation. Spatially explicit capture-recapture models were applied to capture-mark-recapture data to provide population density estimations  (Efford et al.
The spatial-history capture matrix for camera trapping data was then constructed by linking each capture of each individual with the respective coordinates of the camera station and j-occasion, which covered 24 h. Trap detector type 'count' was chosen for the SECR analysis (allowing for multiple detections of the same individual within the same occasion, and including the two camera trapping sessions within the same analysis). We evaluated six different spatial detection functions (half-normal, hazard halfnormal, hazard rate, hazard hazardrate, hazard exponential, exponential), using two different functions for the distribution of home range centres: (i) a Poisson point process  (Borchers and Efford 2008 ) and (ii) a binomial point process  (Royle et al. 2009). We created four habitat masks using (i) the Mean Maximum Distance Moved (MDMM), the average maximum distance between detections of each individual  (Otis et al.
1978) , and (ii) the function SECR which excludes areas inaccessible to cats (open water)  (Oppel et al. 2012). This yielded twenty-four different candidate models using all combinations of detection functions and masks. Root Pooled Spatial Variance (RPSV) was used to measure the dispersion of the sites where individual animals were detected, pooled over individuals  (Calhoun and Casby 1958; Slade and Swihart 1983; Efford 2011). Mean home ranges pre-and post-culling were calculated using MDMM estimations  (O'Connell et al. 2010). SECR models were compared using delta-corrected Akaike Information Criterion (AICc) values and selected using the weighted AIC (AICwt) of each model  (Burnham and Anderson 2002). We then compared home range at individual level between the two sessions. Home range was calculated per individual using a Minimum Convex Polygon estimator (MCP 95%) and the "sf " package (Pebesma 2018), and compared using mean comparison analysis after checking that variance is homogeneous.
Individuals with more than three dots from three different detectors out of alignment were kept. Generalized Linear Models (GLM) were run to test the effect of period on home range size. A Gaussian distribution and 'weights' option were used. Residual homoscedasticity and normality were assessed via Q-Q plots and Shapiro-Wilk tests. All statistical analyses were conducted with R 3. 0. 3 software (R Core Team 2014), using ''ade4''  (Chessel et al. 2004) , "pROC"  (Fawcett 2006 ) "plyr"  (Wickham 2011) , ''varComp''  (Qu et al. 2013) , ''maptools''  (Bivand and Lewin-Koh 2013)  and ''GISTools''  (Brunsdon and Chen 2014)  packages. For all analyses, significant relationships were inferred at α = 0. 05.
Acknowledgements This study was funded by  Province Nord  (Contracts  N°14C330 ,  15C331 ). We are very grateful to  Corentin Chaillon ,  Agathe Gerard ,  Mathieu Mathivet ,  Edouard Bourguet  and  Province Nord landowners  for providing support in fieldwork. We are also very grateful to the veterinary staff of Koné, particularly  Henri Lamaignère  and  Yann Charpentier , for handling the feral cats. We thank  Marjorie Sweetko  for English language editing.
Coccolithophores (calcifying phytoplankton) form extensive blooms in temperate and subpolar oceans as evidenced from ocean-color satellites. This study examines the potential to detect coccolithophore blooms with BioGeoChemical-Argo (BGC-Argo) floats, autonomous ocean profilers equipped with bio-optical and physicochemical sensors. We first matched float data to ocean-color satellite data of calcite concentration to select floats that sampled coccolithophore blooms. We identified two floats in the Southern Ocean, which measured the particulate beam attenuation coefficient (c p ) in addition to two core BGC-Argo variables, Chlorophyll-a concentration ([Chl-a]) and the particle backscattering coefficient (b bp ). We show that coccolithophore blooms can be identified from floats by distinctively high values of (1) the b bp /c p ratio, a proxy for the refractive index of suspended particles, and (2) the b bp /[Chl-a] ratio, measurable by any BGC-Argo float.
Float Measurements and Derived Variables We first searched the BGC-Argo float database for floats equipped with a backscattering and chlorophyll-a fluorescence sensor, as well as a beam transmissometer. We identified two floats drifting in the subpolar Southern Ocean. They operated at a daily frequency and were identified by their World Meteorological Organization numbers (WMO) as 6901583 and 6902738. Afterward, we expanded our analyses to every float equipped with backscattering and chlorophyll-a fluorescence sensors. The ECO Triplet (Three Channel Sensor; WET Labs, Inc. , USA) measures FChl-a and the fluorescence of Colored Dissolved Organic Matter (CDOM) at excitation/emission wavelengths of 470/695 and 370/460 nm, respectively, and the angular scattering coefficient of particles at 700 nm and an angle of 124°, from which we derived b bp at 700 nm  (Boss & Pegau, 2001; Schmechtig et al.
Satellite Data Daily-merged ocean-color satellite data were downloaded from the GlobColour project (ftp://ftp. hermes. acri. fr) with a spatial resolution of 4 km. We described the dynamic of coccolithophore blooms with the [PIC] satellite product using the state-of-the-art retrieval algorithm of  Mitchell et al. (2017)  (see Figure  8c  in  Mitchell et al. , 2017). We further use %PIC defined as follows: %PIC ¼ PIC ½ PIC ½ þ POC ½ × 100; (1) where  [POC]  is the concentration of particulate organic carbon using the retrieval algorithm of  Stramski et al. (2008). In coccolithophore blooms, we appraised the reliability of the band-difference algorithm of  Mitchell et al. (2017)  to perceive changes in b bp by comparing [PIC] to the satellite b bp values from the inverse model of  Maritorena and Siegel (2005).
Matchups Between Satellite and Float Data The thickness of the surface ocean layer detected by satellites can be derived from vertical profiles of downward irradiance acquired by BGC-Argo floats. Unfortunately, 67% of irradiance profiles of floats 6901583 and 6902738 were categorized as "probably bad" quality records following the criteria of  Organelli et al. (2016)  and failed to meet the quality standards for radiometric applications. Therefore, we defined the surface layer sensed by ocean-color satellites as the first 15 m. Bailey and Werdell (2006)  defined matchup criteria between satellite and in situ data for the sake of validating ocean-color satellite radiometric products, which requires that the spatial and temporal difference in acquisition be as small as possible (5 × 5 pixel box at native resolution, narrow time window of <3 h).
Data Availability Statement The data supporting the conclusions of this study are freely available at https://doi. org/10. 17882/76521, and all BGC-Argo data are available at https://doi. org/10. 17882/42182#71394 or ftp://ftp. ifremer. fr/ifremer/ argo/dac/. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www. argo. ucsd. edu, http://argo. jcommops. org).
However, until the recent paper by  Gemmel et al. (2020) , Amalda has remained practically overlooked from the molecular point of view, with sequences of only 5 species deposited in GenBank, essentially originating from the molecular phylogeny of the superfamily Olivoidea by  Kantor et al. (2017). Gemmel et al. (2020)  presented a molecular phylogeny based on the entire mitogenome and long nuclear rRNA gene cassette data, targeting primarily New Zealand species of Amalda. These included species attributed to the subgenera Baryspira P. Fischer, 1883 (including the type species A. australis (G. B. Sowerby I, 1830)  and Gracilispira  Olsson, 1956  (including the type species A. novaezelandiae (G. B. Sowerby II, 1859) ; as well as six Indo-Pacific species, mostly based on the MNHN collections that also form the basis of the present paper.
All analyses were implemented in the Cipres Science Gateway (https://www. phylo. org/) portal ver. 3. 3. (Miller et al. 2010) , using MrBayes ver. 3. 2. 6 on XSEDE and RAXML-HPC2 on  XSEDE (ver. 8. 1. 11). Species delimitation analyses were conducted using the Automatic Barcode Gap Discovery (ABGD) method of  Puillandre et al. (2012)  that uses detection of the 'barcode gap' in the distribution of genetic pairwise distances. The ABGD method was implemented based on all available Cox1 sequences and using the online version of the program (https://bioinfo. mnhn. fr/abi/public/abgd/abgdweb. html) with default parameters. Nodes with Bootstraps values (B) lower than 70% and Posterior Probabilities (PP) lower than 0. 90 are considered non-supported; B-values between 70 to 90 and PP-values between 0.
Consequently, we can expect that ABGD may split the dataset at the population level rather than at the species level. The 17 PSHs supported by both the shell morphology and the genetic clustering were hereafter treated as Secondary Species Hypotheses (SSH). Based on these 17 SSHs, an examination of the published data and a comparison with the type specimens allowed to apply species names to 12 of them (Fig. 1 ). Among the remaining five SSHs, two are represented by multiple sequenced specimens and are described herein as new species, A. miriky sp. nov. and A. alabaster sp. nov. Three more are represented by a single sequenced specimen. Specimen MNHN IM-2007-33287 is very distinct morphologically and several additional, not sequenced specimens are present in our material. A comparison with other known species of Amalda leaves no doubt that these correspond to a new species, which is described here as A.
Distribution Live collected in the Coral Sea; dead specimens also known from Kaimon Maru Bank on Norfolk Ridge. Depth range: alive at 315-360 m, empty shells at 227-487 m. Amalda montrouzieri  (Souverbie, 1860)  Figs Type material Syntypes NEW CALEDONIA • 1 shell; "Insul. Lifu? (Loyalty). Insul. Art?"  [Souverbie 1860a ]; Île des Pins  [Souverbie 1860b ]; MNHN IM-2000-1367 • 3 shells; same collection data as for preceding; MNHN IM-2000-1475 (Fig. 12A-C ).
are closely related, a large genetic distance between them confirms their distinction. Furthermore, there appears to be a minor difference in the radula. In A. montrouzieri the central cusp of the central tooth is clearly shorter than the lateral ones, but in A. alabaster sp. nov. the situation is opposite -the central cusp is distinctly longer. The illustration of the radula of A. montrouzieri by  Kilburn & Bouchet (1988: figs 51, 53)  shows a central tooth with a morphology similar to that in our A. alabaster sp. nov. Regrettably, the authors did not present the station data of the specimen from which the radula was extracted and, therefore, it is not clear whether they indeed examined A. montrouzieri. All our sequenced specimens originated from only two stations off south and south-eastern New Caledonia at depths of 180-210 m, but numerous non-sequenced specimens (see Material) were collected in the depth range of 237-298 m.
Acknowledgements Specimens were obtained during research cruises and expeditions organized by the  MNHN  and  Pro-Natura International  as part of the  Our Planet Reviewed program , and by the  MNHN  and the  Institut de Recherche pour le Développement  as part of the  Tropical Deep-Sea Benthos program  (tERRASSES, EXBODI, EBISCO, KANACONO, KANADEEP 1, NORFOLK 2, SALOMON 2, SALOMONBOA 3, BIOPAPUA, PAPUANIUGINI, MADEEP, KAVIENG 2014, CONCALIS, PANGLAO 2005, NANHAI 2014, ZHONGSHA 2015, MIRIKY), and we are grateful to numerous cruise leaders and co-PIs:  Wei-Jen Chen ,  Laure Corbari ,  Stephane Hourdez ,  Ludivina Labe ,  Peter Kee Lin Ng ,  Karine Olu-Le Roy ,  Nicolas Puillandre ,  Bertrand Richer de Forges  and  Sarah Samadi. All  expeditions operated under the regulations then in force in the countries in question and satisfy the conditions set by the  Nagoya Protocol  for access to genetic resources.
Sofia Zvonareva assisted with photography. The study was conducted using  Joint Usage Center "Instrumental methods in ecology " at the  IEE RAS. We thank  A. Neretina  and  A. Nekrasov  for their friendly and helpful assistance with the SEM facilities. Last but not least we want to thnk the reviewers for very meticulous work and valuable comments. We acknowledge all the partners that have financially supported this work: (1) the  Consortium National de Recherche en Génomique ; (2) the  ANR grant "Bioneocal" (PI: Philippe Grandcolas ); (3) the  Service de Systématique Moléculaire  ( UMS2700 MNHN-CNRS); (4 ) the project " Macrophylogeny of Life " (PI:  Guillaume Lecointre ), part of agreement no. 2005/67 between Genoscope and MNHN; (5) the ATM Barcode (PIs:  Sarah Samadi  and  Jean-Noël  Labat); and (6) the  network "Bibliothèque du Vivant" (BdV)  funded by  MNHN ,  CNRS ,  INRA  and the  Centre National de Séquençage.