dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This study was performed with the financial support of the  ANR SAM "Sources of marine Aerosol in the Mediterranean atmosphere " (grant number:  SIMI-5-6 022 04 ) and used the experimental facilities of the  MEDIterranean center for Marine Ecosystem Experimental Research (MEDIMEER) ,  CNRS Institute of Ecology and Environment (InEE). The authors thank the staff of  STARESO (Station de Recherche Sous-Marine et Océanographique)  for their hospitality and assistance in the field. Authors wish to acknowledge the support of the MISTRAL-CHARMEX and MISTRAL-MERMEX projects and to thank  Elise Hatey  and the  MARBEC Cécile Roques  and the MARBEC technical pole for HPLC analysis and TEP measurement. We would like to thank  Christophe Salmeron  and  David Pecqueur  from the  UPMC-CNRS  cytometry and imaging platform of the  Banyuls Oceanographic Observatory as well as Patrick Raimbault , leader of the MIO PAPB analytical platform.
For one putative individual of L. purpurea collected on the island of Ilovik, Croatia (44. 445389°N, 14. 57625°E) in October 2014 only morphological data were taken, as DNA quality proved to be insufficient for PCR and sequencing. Morphometric and meristic measurements followed Hofrichter (unpublished * ). Total length (TL), standard length (SL), head length (Hl), body depth (Bd), body width (Bw), sucking disc length (SDl), sucking disc width (SDw), interorbital distance (iO) and number of papillae in sucking disc regions A, B, C (papA, papB, papC) was measured/ counted in two individuals of putative L. purpurea and L. lepadogaster from Greece, and one putative L. purpurea individual from Croatia. Voucher specimens of all individuals used for morphological analysis were deposited at the Natural History Museum Rijeka, Croatia (L.
purpurea voucher IDs: PMR DNA was extracted from fin tissue using a rapid Chelex protocol  (Richlen and Barber 2005). A 390 bp long fragment of the third domain of the mitochondrial 12S rDNA and a 577 bp long part of the mitochondrial COI gene were amplified and sequenced according to the protocols described in  Henriques et al. (2002)  and  Duftner et al. (2005) , respectively. The primer pairs used for PCR and chain termination sequencing were 12sFor/12sRev  (Henriques et al. 2002)  and FishF1/ FishR1  (Ward et al. 2005)  for 12S and COI respectively. DNA fragments were purified with Sephadex TM G-50 (GE Healthcare) and visualized on an ABI 3130xl capillary sequencer (Applied Biosystems). In addition, following sequences were downloaded from GenBank and added to the dataset: for 12S rDNA AY036587 and AY036589-AY036605  (Henriques et al.
2002) , and for COI KF369136, KJ616457 and KJ768244-KJ768246  (Lobo et al. 2013 , Conway et al. 2014 , Landi et al. 2014). Sequences were aligned in MEGA 6. 0  (Tamura et al. 2013 ) using MUSCLE  (Edgar 2004 ). All newly generated sequences are deposited in GenBank under the accession numbers MF425769-MF425781 and MF544114-MF544120. Some sequences from Greece (of the individuals also used for morphological analysis) are also available from BOLD (project MEDLP, Mediterranean Lepadogaster purpurea). For phylogenetic tree inference, sequences were collapsed into haplotypes. Unrooted maximum likelihood (ML) trees were inferred in PhyML 3. 0  (Guindon et al. 2010) , employing the best fitting substitution models selected based on the Bayesian Information Criterion (BIC) in MEGA. Statistical support was assessed from 1000 bootstrap replicates.
POLARIS is particularly useful when advected aerosol layers in the free troposphere are coupled to the PBL, because the lidar depolarization ratio profiles provide information about the particle shape, allowing for the discrimination among different aerosol types. Furthermore, PO-LARIS improves the z PBL detection since the computation of δ (based on the ratio of two lidar signals) partially cancels out the incomplete overlap effect, allowing for the z PBL detection at lower heights rather than using methods based exclusively on the RCS (affected by incomplete overlap). Data sets of lidar and microwave radiometer measurements registered in the ChArMEx (Chemistry-Aerosol Mediterranean Experiment; http://charmex. lsce. ipsl. fr/) experimental campaigns during the summers of 2012 and 2013 are used in this study for the POLARIS evaluation.
, 2013). The inversion algorithm is based on neural networks  (Rose et al. , 2005)  trained using the radiosonde database of the Murcia WMO station no. 08430 located 250 km from Granada. The accuracy of the temperature profiles is 0. 8 K within the first 2 km and 1. 5 K between 2 and 4 km. The altitude grid of the inversion increases with height: 30 m below 300 m a. g. l. , 50 m between 300 and 1200 m a. g. l. , 200 m between 1200 and 5000 m a. g. l. , and 400 m above 5000 m a. g. l. (Navas-Guzmán, 2014). The MWR temperature profile is used to locate the z PBL (z MWR PBL ) by two algorithms. Under convective conditions, fuelled by solar irradiance absorption at the surface and the associated heating, the parcel method is used to determine the mixing layer height (z MWR ML ; Holzworth, 1964).
WRF validation using POLARIS and MWR Recent studies use the z PBL determined using lidar data to validate the z PBL obtained from the WRF (Weather Research and Forecasting) model (z WRF PBL ;  Xie et al. , 2012; Pichelli et al. , 2014 and Banks et al. , 2015). In this section, we take the advantage of the z PBL determined by POLARIS (z POL PBL ) together with the microwave radiometer z MWR PBL during CHArMEx 2012 and 2013 to validate the z WRF PBL.
WRF model setup The model configuration consists of four nested domains with 27, 9, 3 and 1 km (approximately) spatial resolution domains, respectively, and 50 vertical levels. The outputs (i. e. temperature, wind and humidity profiles) of the 1 km domain are analysed. The initial and boundary conditions for the WRF model runs are taken from the NCEP (National Centers for Environmental Prediction) high-resolution Global Fore-cast System data set (http://www. emc. ncep. noaa. gov) every 6 h. The choice of the model's physical parameterization is based on the results of previous evaluation studies conducted in the study area  (Arbizu-Barrena et al. , 2015). Particularly, the Mellor-Yamada-Nakanishi-Niino level 2. 5 model is selected for the PBL parameterization  (Nakanishi and Niino, 2009). The parameterizations used for the rest of physical schemes are the Eta (Ferrier) microphysics parameterization scheme  (Rogers et al.
Acknowledgements. This work was supported by the  Andalusia Regional Government  through project  P12-RNM-2409 , by the  Spanish Ministry of Economy and Competitiveness  through projects  CGL2013-45410-R  and  CGL2016-81092-R , and by the  European Union 's  Horizon 2020 research and innovation programme  through project  ACTRIS-2  (grant agreement no. 654109 ). The authors thankfully acknowledge the  FEDER program  for the instrumentation used in this work. This work was also partially funded by the  University of Granada  through the contract " Plan Propio. Programa 9. Convocatoria 2013 " and by  EU COST ES1303 (TOPROF). The authors express their gratitude to the  ChArMEx  project of the  MISTRALS (Mediterranean Integrated Studies at Regional and Local Scales ; http://www. mistrals-home.
Metagenomics Sampling, Sequencing, and Assembly Sediment samples for metagenomic analyses were collected from the A site in GBS on 2 December, 2008  (Cole et al. , 2013). DNA was extracted from sediments using the Fast DNA Spin Kit for Soil (MP Biomedicals, Solon, OH, United States). Library preparation and sequencing of the GBS metagenome using the 454-FLX platform with Titanium chemistry (Roche, Branford, CT, United States) was performed at the Joint Genome Institute, and reads were assembled with SPAdes using kmer values of  55, 77, 99, 111, and 127 (Bankevich et al. , 2012). Genome completeness and gene duplications (i. e. , potential contamination) were estimated with CheckM  (Parks et al. , 2015)  software. GBS metagenome assemblies and SAG accession numbers are reported in Supplementary Table  S1.
Read-Binning Based on Machine Learning and Prediction of Metabolic Functions Metagenomic reads were binned by nucleotide trimer frequencies using the Multi-Layer Perceptron (MLP) machine learning package in WEKA version 3. 6  (Witten et al. , 2011)  as described in detail in  Becraft et al. (2015). The MLP was initially trained using nucleotide frequencies of clipped genomic segments of 2,000 bp from the Calescamantes SAG coassembly. In addition to the NAG1 SAG co-assembly, SAGs representing other known populations in GBS [Fervidibacteria (JGI_0000001-G10), Thermoflexus hugenholtzii (JGI_2140918011)  (Dodsworth et al. , 2014) , Calescamantes (JGI_2527291514), and Aigarchaeota (JGI_2264867219)], were included to provide multiple points of reference for the algorithm  (Rinke et al. , 2013). Metagenomic reads were assigned to the GBS Calescamantes population if their MLP confidence score was >0.
9 (a score of 1 indicates 100% confidence), assessed as the point at which false positives were minimized while maximizing true positives. The annotated genes from the NAG1 SAG co-assembly were searched against the unassembled GBS metagenomic nucleotide database using BLAST  (Altschul et al. , 1990) , and matches with an e-value ≤ 1E-15 were also included in the assembly after removal of redundant sequences. MLP-assigned (32,699) and BLAST-identified (5,323) reads were assembled as described in  Becraft et al. (2015) , and assembled contigs were uploaded to RAST  (Aziz et al. , 2008)  for gene calling and a combination of RAST and BlastKOALA (KEGG)  (Kanehisa et al. , 2016)  were used for annotation and metabolic mapping. Functional analyses of select proteins were also predicted using CDD/SPARKLE  (Marchler-Bauer et al.
, 2017). CRISPR regions were identified with CRISPRfinder at http://crispr. i2bc. paris-saclay. fr/Server/ (Grissa et al. , 2007). Individual SAG data are located at http: //microbialdarkmatter. org (Supplementary Table  S1 ). The NAG1 metagenome assembled genome was deposited in the Integrated Microbial Genome database (IMG genome ID 2751185538) 1.
16S rRNA Analysis NAG1 SAG 16S rRNA gene sequences were queried against the GenBank NCBI-nr database using BLAST to identify the nearest sequenced relatives. 16S rRNA gene sequences within ∼85% nt identity to NAG1 sequences, as well as more distant taxa and 16S rRNA gene sequences, were aligned with SILVA (SINA package)  (Quast et al. , 2013)  (Supplementary Figure  S1 ). Maximum-likelihood phylogenies were generated using Mega 6. 0 using the General TimeReversible (GTR) Model, with Gamma distribution with invariable sites (G+I), and 95% partial deletion with 1000x bootstrapping  (Tamura et al. , 2013). ANI and average amino acid identity (AAI) were calculated using the calculator at http://enve-omics. ce. gatech. edu/ani/ (Goris et al. , 2007).
, unpublished data; Kozubal et al. , 2013). The GBS SAG 16S rRNA gene sequence was 91% identical to sequences from iron springs in Norris Geyser Basin. Since this value is below the median identity separating existing bacterial families  (Yarza et al. , 2014) , the GBS NAG1 lineage likely represents a novel family. Interestingly, NAG1 16S rRNA gene sequences at GBS were 98% identical to a partial sequence obtained from a circumneutral hot spring in the Bechler region in YNP (498 bp: GenBank ID KP091636. 1), which confidently claded with the GBS NAG1 sequence, possibly representing another "species"-level population within this family lineage  (Colman et al. , 2015). This was the first identification of NAG1 organisms in a circumneutral hot spring. The sequence from the Bechler region hot spring was not included in Figure  1  due to shorter length.
, 2011). When NAG1 bidirectional fructose 1,6-bisphosphate aldolase/phosphatase sequences were queried using BLAST against the NCBI-nr database, all assemblies showed high similarity (99% cover and 68-70% identity) to those from members of the candidate phyla Bathyarchaeota and Odinarchaeota, as well as members of the class Thermoprotei, which is generally consistent with the phylogenetic placement of NAG1  (Du et al. , 2011; Kozubal et al. , 2013; Adam et al. , 2017). This suggests that these genes were likely vertically inherited and not recently acquired. Both YNP and GBS assemblies contained genes necessary for a complete TCA cycle. All assemblies harbor a NAD-dependent malate dehydrogenase (E. C. 1. 1. 1. 37), with the YNP harboring an additional malate:quinone oxidoreductase (E. C. 1.
, 2009) , there might be more competition for oxygen at One-Hundred Springs Plain Spring or NAG1 populations may inhabit deeper sediments. In both RAST and BlastKOALA annotations, GBS NAG1 assemblies appear to have the ability to ferment pyruvate to ethanol by oxidizing pyruvate to acetyl-CoA [2-oxoglutarate/2-oxoacid ferredoxin oxidoreductase (E. C. 1. 2. 7. 3)], and further to acetate [acetyl-CoA synthetase; both ADP-forming (E. C. 6. 2. 1. 13) and non-ADP-forming (E. C. 6. 2. 1. 16)], acetaldehyde [NAD+ aldehyde dehydrogenase (E. C. 1. 2. 1. 3)], and finally to ethanol (alcohol dehydrogenase). The YNP NAG1 assembly encoded for alcohol dehydrogenase and non-ADP-forming acetyl-CoA synthetase, it did not code for ADP-forming acetyl-CoA synthetase or aldehyde dehydrogenase (NAD+). This could indicate that the absence of the higher affinity cytochrome bd ubiquinol oxidase complex in NAG1 at GBS promotes a complete fermentative metabolism under hypoxic conditions.
Cofactors, Vitamins Metabolism, Mineral Acquisition, and Viral Defense All NAG1 assemblies contained SUF genes involved in sulfur assimilation [sufB (K09014), sufC (K09013), and sufF (E. C. 2. 8. 1. 7)], which is the sole method for FeS assimilation in these organisms, and all assemblies contained the fhuD gene involved in external Fe acquisition (K02016); the YNP assembly contained all fhu genes (fhuDBC). Additionally, genes involved in phosphate uptake  [pstSCAB (E. C. 3. A. 1. 7. 1) and ferrous-iron efflux transport fieF (K07243)] were annotated in all assemblies. All NAG1 assemblies also contained enzymes involved in co-factor production including thioredoxin  [trxA and trxB (K03671 and E. C. 1. 8. 1. 9 )], adenine (E. C. 3. 5. 4. 2) and lipoic acid (K03800), and contained pathways for oxygen stress and heavy metal detoxification, including superoxide dismutase (Fe-Mn family) (E.
C. 3. 6. 3. 4), Na + /H + ATPase transporters (K02123), and multiple unique genes involved in potassium uptake and homeostasis (trk genes) (e. g. , K03499), which are necessary for growth in less acidic conditions  (Padan et al. , 2005). Other notable genes annotated in the NAG1 assemblies at GBS include a CoB-CoM heterosulfide reductase (hdr) (E. C. 1. 8. 98. 1), which catalyzes the reversible reduction of the heterodisulfide of the methanogenic thiol enzymes coenzyme M (CoM-SH) and coenzyme B (CoB-SH). Because other genes involved in methanogenesis were not found, the role of hdr in the NAG1 is not clear, but it may be involved in reduction of other disulfide-containing compounds. Populations at GBS lacked numerous genes involved in protein metabolism, including those for chorismate metabolism, a precursor to tryptophan, as well as biosynthetic pathways for tyrosine and phenylalanine.
2003). One can also consider an increasing degree of nonlinearity using phase-based MAR models (also called "Threshold AR" models,  Stenseth et al. 2015) , in order to examine in detail the plausibility of nonlinear coexistence mechanisms. Most plankton-based MAR analyses so far (but see  Huber and Gaedke 2006)  have aggregated data at the class level, therefore preventing any attempt to examine the strengths of competitive interactions among species or genera. Recent papers highlight that a finer taxonomic resolution would allow for a better understanding of community dynamics  (Griffiths et al. 2015; D'Alelio et al. 2016 ). Here, we take take advantage of a long-term dataset of coastal phytoplankton monitoring (>20 years of biweekly counts) with fine taxonomic resolution -to the genus level -to investigate: (1) What are the abiotic drivers of population growth (i.
Phytoplankton data The National Phytoplankton and Phycotoxin Monitoring Network (REPHY 1 ) involves plankton collection every two weeks, within two hours at high tide (for more details, see Appendix S1: Section S1. 2). Data collection started in 1987 in AB (Teychan), so the full dataset consists in 709 sampling dates. The full dataset is used in all analyses except for multivariate autoregressive models including all planktonic groups (see "MAR(1) models"): as cryptophytes were not recorded properly before 1996, and this could affect the counts of other taxa, we focused on observations post-1996 for MAR(1) models. Taxonomic units were aggregated at the genus level following previous work on this and 1 http://envlit. ifremer. fr/infos/rephy_info_toxines other plankton datasets  (Hernandez et al.
Abiotic data Environmental variables collected at Teychan and Buoy 7 included water temperature (TEMP in °C), salinity (SAL, in g kg -1 ), nutrients (ammonia NH4 + , silicates Si(OH) 4 , phosphates PO 3- 4 and nitrates-nitrites NO x =NO 2 + NO 3-, all in µmol L -1 ) and suspended particulate matter (SPM, in mg L -1 ), with an organic part assessment (SPOM, in mg L -1 ). They were measured following  Aminot and Kérouel (2004, 2007)  (see Appendix S1: Section S1. 2 for more details). Some abiotic variables (nutrients and SPM) could not be recorded at Teychan until 2007. Their values were therefore replaced by measurements at the closest hydrological station, Tès (44°39'59 N / 1°08'40 W; 1. 4 km from Teychan), which shows a similar hydrodynamic functioning (see Appendix S1: Fig. S3 ). For Buoy 7, all abiotic variables could be measured onsite for each year.
Daily meteorological data -rainfall (mm), irradiance (J cm - 2  ), wind direction and velocity (m s -1 ) -were provided by Météo France for the nearby Cap Ferret station (44°37' N / 1°14' W) and were used as inputs for both sampling stations. Wind energy was extracted from these data as the squared velocity (m 2 s -2 ). Daily North Atlantic Oscillation (NAO) index was downloaded from the National Oceanic and Atmospheric Administration (NOAA) Weather Service for Climate Prediction Centre website 2. Monthly Atlantic Multidecadal Oscillation (AMO) index was downloaded from the NOAA Physical Science Division website 3. All environmental data were linearly interpolated on the same dates as plankton sampling dates, removing missing values (see Appendix S1: Fig. S3 ). We also transformed abiotic variables to represent meaningful biological and physical processes.
We summed NH + 4 and NO x as nitrogen suppliers (Ntot). We integrated inflow (CumDebit), rainfall (CumPrec) and irradiance (CumRg) between t -1 and t to represent the growth conditions of plankton groups between two sampling dates  (Glé et al. 2007). NAO was averaged over the same period and a variable summarizing the effect of wind was constructed using the mean wind energy over 3 days (F. Ganthy, Ifremer, personal communication). All datasets and R scripts for analyses are available online in a GitHub repository 4.
We fitted a log-linear AR(p) model, which allowed the determination of the maximum lag order p. The shape of growth-abundances curves (see Appendix S2: Fig. S1 ) and analyses of residuals (Appendix S2: Section S2. 5) confirmed that log-linearity on a log-scale -Gompertz rather than Ricker growth  (Ives et al. 2003)  -was an appropriate approximation. We used the arima function from the stats package (Venables and Smith 2013) in R (version 3. 3. 2) to estimate coefficients and corresponding Akaike Information Criterion (AIC) for each AR(p) model. The number of time lags considered varied between 1 and 30, the maximum reasonable value for the length of our dataset (780 dates for taxa monitored after 1996). The likelihood was estimated using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method, an approximation of Newton's method, with a maximum of 100 iterations.
All abiotic variables were then transformed to take into account seasonality. First, a seasonal component was computed from a linear regression of temperature data against trigonometric functions with an annual frequency (Appendix S1: Section S1. 7). Abiotic variables were then regressed against this seasonal component and residuals from this regression were added to the model as abiotic variables (see Eq. 2). T t = a T + S 1 cos(ωt) + S 2 sin(ωt) + T,t X v,t = a v + b v (S 1 cos(ωt) + S 2 sin(ωt)) + η v,t (2) where T t is the temperature at time t, S 1 cos(ωt) + S 2 sin(ωt) is the seasonal component with a normal noise T,t and η v,t is the residual component of covariable X v,t when season has been taken into account, a v and b v being associated regression coefficients. Model evaluation was performed in two steps.
the iteration number had to be below 0. 001 to accept convergence. We thoroughly tested our model selection approach using 10 simulated datasets resembling our empirical dataset, for 3 contrasted ecological scenarios: (1) effects of environment only, (2) effects of interactions only, and (3) both environment and interactions (see Appendix S2: Section S2. 1). The results on simulated data showed that AICc selected the appropriate model in each scenario, thus there was no need for more complex model selection using AICb or refined procedures as did  Ives et al. (2003)  (results are provided in Appendix S2: Section S2. 1). Non-linear dynamics: bivariate phase-dependent (TAR) models Complex nonlinear dynamics (sensu  May 1974; Turchin 2003)  may not be completely captured by linear models, even in the logarithmic scale  (Stenseth et al.
Environment-based phases -storage effect Defining a environmental-based TAR model when the environmental conditions are multidimensional requires two main choices: the choice of a unifying variable that can summarize external conditions of the system (i. e. , the phase) and the definition or estimation of a threshold for such a variable. The number of different phases, and therefore thresholds, is itself debatable. We chose to use 2-phase hierarchical clustering based on Ward's method (see Appendix S5: Section S1. 1 for details). Two phases allowed to differentiate between low chlorophyll and salinity and high chlorophyll and salinity conditions, while making results comparable to both previous sections and other works in the literature  (D'Alelio et al. 2015). Using this clustering method to define a phase for both Teychan and Buoy 7 datasets, a different MAR model was fitted to each phase.
In our case, the reasons for such stabilizing niche differences are yet to be discovered. Whether other studies using long time series, in plankton or other similar systems find results close to ours, or manage to get near-neutral dynamics, would be of general interest. If coexistence occurs through stabilizing niche differences, we note that observing intergroup interactions that are very small may be in fact logical: the criteria for coexistence in theoretical Lotka-Volterra communities require intergroup competition to be much lower than intraspecific competition in large communities  (Barabás et al. 2016). Mutshinda et al. (2016)  recently reported on ecological equivalence -neutrality -between groups using like us a long-term phytoplankton dataset (the L4 dataset in the English Channel).