dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Statistical analyses were performed using R software (version 3. 2. 3) (R Development Core Team, 2016). Herbage (grazing management, herbage quality), soil (Ti content) and animal (OMI and DMI, soil intake) variables were tested using linear mixed models with treatment and period as fixed effects, and individual as a random effect. Individual animals were used as the experimental unit. Normality of residuals was checked using Shapiro-Wilk tests. The levels of studied factors were compared by the means using the 'glht' function of 'multcomp' package (Tukey's correction) (P < 0. 05).
Acknowledgements The experiment was partially funded by  Plan National Chlordécone 3  ( CHLORDEL  project). The authors thank warmly  J. Fleury ,  F. Pommier ,  F. Nimir  and  F. Labirin  from  INRA PTEA ,  S. Calif  and  T. Silou  from  INRA URZ , as well as  Y. Le Roux  from  UR AFPA  for statistical advice and  D. J. Beriro  from  British Geological Survey  for English proofreading.
g. , 5 s) from which the predictor variables are then calculated (i. e. , the predictor variables summarize the acceleration data of the segment, and each segment with its set of predictor variables constitutes an observation). When acceleration is already sampled in bursts, the original bursts or subsegments are used for parameterization. Modeling is usually based on supervised classification models that are trained with a ground-truthed dataset  [7] [8] [9]. In this case, the behavior of tagged animals must be observed in the field, which allows for assigning a behavioral category to each segment of the training dataset. Alternatively, unsupervised clustering combined with a subsequent expert-based interpretation of the identified clusters can be used  [10]. The resulting model can then be used to automatically detect behavior.
To increase the amount of acceleration data during field observations, some collars were rescheduled to measure acceleration bursts every 15 s for the observation period. Almost all observations were done on meadows due to the low visibility inside the forest. We documented the observed behavior with a video camera (Panasonic Lumix DMC-TZ 10, Panasonic HC-V500). Before or after each observation, a digital radio-controlled clock, which automatically synchronized to the German DCF77 time signal, was filmed to facilitate easy synchronization of acceleration data and video segments.
Data processing In the laboratory, we first synchronized the acceleration signals with the video recordings and allocated a structural behavior category to each acceleration measurement. For this purpose, we used the programs Windows Movie Maker 5. 1 in combination with R  [28]  and the program Observer ® XT 10 by Noldus Information Technology. We described behavior based on the structural behavior categories: lying, standing, browsing, walking, trotting, galloping and 'others' (Table  1  with detailed description). Additionally, we classified the posture of the animal's neck in head down, head middle, head up and head moving (Table  2  with detailed description), because roe deer can display all behaviors with different neck postures. All behavior and posture categories were exhaustive and exclusive.
The acceleration data were sampled in units of millivolts. We transformed the raw acceleration data in units of g (1 g = 9. 81 m/s 2 ) with a linear transformation following the user manual  [22]. As every sensor is a little different, it is recommended to calibrate each collar by successively aligning every axis perpendicular toward the earth's gravitational field. This allows for calculating sensor-specific offset and slope (see detailed description in  [22] ). We were not able to do the calibration for half of the collars, because we did not calibrate them before collaring the animals and afterward did not get all collars back from the field. Instead, we used the average calibration values of 30 calibrated collars as constants for the linear transformation. Calibration revealed small differences in the collar-specific offsets which influenced measurements of static body acceleration.
2 ). Static body acceleration was calculated with a moving average using a window width of seven data points (0. 66 s). This window width showed fine smoothing when animals changed posture  [25]. All other predictor variables were calculated with window widths of 10 data points (~1 s, labeled as w1), 22 data points (~2 s, labeled as w2), 45 data points (~4 s, labeled as w5) and 96 data points (~9 s labeled as w9).
Modeling The modeling approach consisted of three modules. In the first module, we developed and evaluated a classification model that classified the observed acceleration signal into structural behavior categories. We expanded this module with another classification model that targeted the posture of an animal's neck. In the second module, we developed a model to synthesize the predicted sequence of structural behaviors into the internal states active and    shows static body acceleration (sba, calculated with a window width of seven data points); bottom right shows mean dynamic body acceleration (mba_w2, calculated with a window width of 22 data points) of the same burst (see Table  3  for a detailed description of the predictor variables). In the first 5 s the animal is browsing with the head below shoulder height.
Afterward, the head is raised above shoulder height and the animal is standing resting. In the third module, we combined the previous modules to predict vigilance behavior. Previous studies that tested different classification techniques to classify behavior from acceleration data did not find substantial differences in terms of model performance  [8, 26]. Therefore, we employed classification trees (CART algorithm, R package rpart  [27, 28] ) which are relatively fast-running and straightforward to interpret. The amount of behavior-tagged acceleration data differed between the observed animals (Additional file 3: Fig. S3 ). In case of differences in the individual acceleration signatures, this would have caused biased splitting rules toward animals with more data. Hence, we homogenized the dataset and randomly selected a subset of 1500 data points per behavior category and animal.
We evaluated the minimum amount of data points per subset and observed a stabilization of the splitting rules after 1000 data points. For some animals, we had less than 1500 data points for the behavior categories trotting, galloping and 'others'. Here, we used all available data points. The resulting dataset was split into 2/3 training and 1/3 validation datasets. We were not particularly interested in the behavior category 'others' and reduced the weight (model parameter 'weight') for these data points until 'others' behavior was classified into a single leaf. Preliminary testing showed that model calibration based on random tenfold cross-validation led to substantial overfitting of the models. We therefore used cross-validation based on the individual animals to determine the optimal tree size.
Here, the model is trained successively for x -1 animals and tested for the out-of-bag animal. We let the classification tree grow to full size and then pruned it to the smallest tree whose cross-validation error is less than the minimum cross-validation error plus one standard deviation ('1-SE rule, '  [27] ). We evaluated model performance by predicting the pruned classification model to the validation dataset and calculated model accuracy for each animal separately. Furthermore, we developed and evaluated a classification model that predicted the posture of the animal's neck (posture model). For this model, we selected parameters that reflect neck posture (sba_x, sba_x_c and sba_z) as predictor variables and excluded data points when the head was moving (posture 'head moving'). We applied the same model procedure as for the behavior model.
Results We documented all behavior categories of interest for eight animals in the field. Five additional animals were observed just for short periods of time with an incomplete set of behaviors and were therefore not considered for this analysis. In total, we behavior-tagged more than 15 h of acceleration data. The amounts of behaviortagged acceleration data differed substantially between the animals (Additional file 3: Fig. S3 ). Browsing and lying were observed most frequently. The final classification model discriminated all behaviors of interest (Fig. 3 ; cross-validation plot: Additional file 5: Fig. S5 left ). The model separated the behaviors standing and lying from the active behaviors by the mean dynamic body acceleration (mdba_xyz_w2). Browsing behavior was subsequently split by the corrected static body acceleration of the x axis (sba_x_c) and, thus, by the posture of an animal.
The behaviors walking, trotting and galloping were again discriminated by predictor variables that correspond to the dynamic body acceleration. Applied to the test dataset, the classification model correctly predicted all behaviors in 71% of the cases (range between animals 66-79%). The confusion matrix (Table  4 ) revealed that most erroneous predictions were distributed in behavior categories that are characterized by similar body posture and dynamic body acceleration. Thus, the model mixed standing with lying, browsing with walking and trotting with galloping. Errors concerning the behavior 'others' were distributed over all behaviors. For most behavior categories, sensitivity and positive predictive value were comparable between animals (Fig. 4, left). Optimal tree size of the posture model according to the one standard deviation rule was 12 (Additional file 5: Fig.
Instead, we aimed at constructing one population-based model and controlled potential bias by implementing collar-specific parameter correction (parameter sba_x_c), homogenization of the training dataset and cross-validation based on the animals. Additionally, we calculated animal-specific performance parameters for each behavior category. These were in similar ranges, indicating that overfitting toward individual animals was hardly present. Only for a few animals we observed varying values for the behaviors trotting and galloping, which were most likely caused by the low number of data points. The behavior category 'others' was less predictable. Here, we pooled several behaviors despite their divergent acceleration signatures. These probably interfered with the acceleration signatures of the main behavior categories.
Conclusion By predicting behavior, state and vigilance to an exemplary dataset, we demonstrated diverse applications and the huge potential behind acceleration data that can be assembled with the presented approach. In particular, continuous monitoring of vigilance behavior, in combination with detailed behavior budgeting and spatiotemporal allocation of both, will provide new insights in how large herbivores perceive and control predation risk  [14, 17] , will allow to test scientific hypotheses and models [e. g. , 37] and, additionally, will provide helpful tools for wildlife management [e. g. , 38].
Acknowledgements First of all, we thank the  German Academic Scholarship Foundation (Studienstiftung des Deutschen Volkes ) for supporting MK with a Ph. D. scholarship. We also thank  Noldus Information Technology  for providing their software 'Observer XT. ' We are grateful to all people that assisted in the field. Finally, we thank  Stephanie Witczak ,  Benedikt Gehr ,  Lino Kämmerle ,  Luca Corlatti  and two anonymous reviewers for their insightful and constructive comments.
The purification protocol consisted of successive streakings on solidified BG11based culture medium until obtaining an axenic culture  (7). The genomic DNA of ULC007 was sequenced in two runs. The first run was performed on an Illumina HiSeq 2500 System (Illumina, Inc. , San Diego, CA, USA), which generated 4,404,753 paired-end reads of 100 bp. The second run, performed using the Illumina MiSeq System (Illumina Inc, San Diego, CA, USA), generated 440,221 paired-end reads of 250 bp. Raw reads from both runs were processed and de novo assembled using Velvet v1. 2. 10 (8) and SPAdes v3. 5  (9)  after a thorough analysis of the kmer length distribution using kmergenie v1. 6982  (10). The resulting sets of contigs were integrated using the CISA v1. 3 software  (11). The accuracy of the final assembly was evaluated using the REAPR v1.
0. 18 software  (12). Contigs were annotated using glimmer3 v3. 02  (13)  and a reference database composed of 260 cyanobacterial genomes extracted from the PATRIC database. The presence of secondary metabolite biosynthesis gene clusters was tested using antiSMASH v3. 0  (14). The draft genome of ULC007 is composed of 5,684,389 bp distributed into 118 contigs, with an average GϩC-content of 48. 6%. Final assembly yielded an L 50 value of 12 (L 90 ϭ 51) and an N 50 value of 152,457 bp (N 90 ϭ 27,655 bp). A total of 5,604 protein-encoding genes (PEGs) were identified by glimmer3, of which 4,785 PEGs correspond to proteins already characterized in other cyanobacterial genomes, whereas 1,244 PEGs are apparently new. The genome contains a variety of PEGs involved in stress response (e. g. , molecular chaperones DnaK and DnaJ, DNA gyrase, putative cold shock proteins of the Csp-family).
The main components (KaiA, KaiB, and KaiC) of the cyanobacterial circadian clock were found, as well as PEGs related to the circadian input (CikA, LdpA, Pex) and output (CikA, SasA, RpaA, LabA) pathways  (15). Finally, three unknown NRPS-, NRPS/PKS-, and PKS-like gene clusters were identified, whose exploration may potentially lead to the discovery of novel secondary peptide metabolites. This genome is the first Antarctic cyanobacterial genome ever determined, and undoubtedly will contribute to our understanding of the mechanisms underpinning the survival and ecological success of cyanobacteria in high latitudes. Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession MPPI00000000. The version described in this paper is version MPPI01000000.
ACKNOWLEDGMENTS We thank  Dominic Hodgson  ( British Antarctic Survey ) for providing the Antarctic mat sample and  Arnaud Taton  and  Stana Grubisic  for the strain isolation. This work was supported by the  BELSPO  project  CCAMBIO  ( SD/BA/03A ). I. S. P. and L. C. had a PhD FRIA fellowship from the  FRS-FNRS (National Fund for Scientific Research) , and now L. C. has a PhD fellowship from  Planet TOPERS IUAP (Inter-university attraction poles ),  BELSPO  project ( Belgian Science Policy ). E. J. has funding from  Planet TOPERS IUAP  and  ERC StG ELITE. A. M. was partially financed by the bilateral Scientific and technical cooperation between the  Republic of Poland and French-speaking Community  and the  Walloon region of Belgium  ( 2014-2016 ). A. W. is a research associate of the FRS-FNRS.
We have analyzed synoptic underway data collected onboard two Voluntary Observing Ships (VOS) that sailed across the North Atlantic during boreal spring 2010 (Monte Olivia/Rio Blanco sailing the route France-Brazil and Colibri sailing the route France-French Guyana; see Fig. 2  upper panels and Supplementary Table  S1  for details on the route of the vessels). We have also used the data collected onboard these VOS during boreal spring of other years for comparison. The aim of this study is to identify the extension and quantify the impact of the SST anomaly observed in the North Atlantic climatic event of boreal spring 2010 (Fig. 1 ) with regard to the CO 2 uptake capacity of the basin.
Data collected between 35°N and 10°S were used. A total of 16 voyages recorded data in the selected region and for the selected period (Supplementary Table  S1 ). Due to technical problems, the cruise performed during April 2010 by the Rio Blanco only recorded data from 10°S until 6. 4°S. Thus, for the remaining cruises performed during April along the Rio Blanco track, only data recorded from 10°S to the equator are used for comparison. The atmospheric molar fraction of CO where pH O 2 is the water vapor pressure at 100% humidity calculated from SST and SSS and C is the fugacity coefficient calculated from Weiss  29. The cruise performed by the Colibri during May 2007 lacked P data due to a malfunctioning of the barometer. The monthly atmospheric pressure of the NCEP/NCAR reanalysis project was used in this case.
fCO 2atm derived from the NOAA/ESRL Global Monitoring Division data was also calculated for the remaining ship tracks used in this study for comparison. Good agreement between the fCO 2atm measured onboard and that obtained from the NOAA/ESRL Global Monitoring Division data was verified, with a statistical difference of 0. 76 ± 0. 02 μ atm among both (paired t-test). Sea-air CO 2 fluxes (F) were calculated according to where S o is the solubility of CO 2  29  and k is the gas transfer velocity. k was calculated according to 30 : = - F kS (fCO fCO ) (2) =. - Sc k 0 27U ( /660) (3) 10 2 0 ,5 where Sc is the Schmidt number. U 10 was obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) monthly reanalysis data set (ERA-interim, 0. 25° resolution) and interpolated at the position of the underway measurements.
Monthly wind, atmospheric pressure (NCEP/NCAR reanalysis project) and SST climatologies were computed for the period 1994-2013. Monthly wind and SST anomalies at the basin scale were then computed for February to May 2010. SST anomalies were also computed along the ship tracks by comparing the measured SST with the interpolated, climatological SST. For basin scale S o and pH 2 O calculations, SSS data derived from the Soil Moisture and Ocean Salinity (SMOS) mission and obtained from the Ocean Salinity Expertise Center (CECOS) of the CNES-IFREMER Centre Aval de Traitemenent des Données (CATDS), at IFREMER, Plouzané (France) were used. The available monthly composites (2010 to 2014) were used to calculate the climatological SSS. CO 2 flux normalization and CO 2 flux anomalies calculation. The different parameters that we considered to potentially contribute to the sea-air CO 2 flux anomaly of 2010 are SST, SSS, wind intensity, fCO 2atm and fCO 2sw.
Acknowledgements This research was supported by the European Integrated Projects CARBOOCEAN (contract  511176-2 ) and  CARBOCHANGE  ( FP7 264879 ),  Atlantos  (grant agreement  633211 ), the  BIOAMAZON  project ( AIRD Guyamazon 01739/13 ), the  DICAM  project ( CAPES CM II 1975/2014 ) and the  Institut de Recherche pour le Développement (IRD). Sea surface salinity data derived from thermosalinograph instruments installed onboard voluntary observing ships were collected, validated, archived, and made freely available by the  French Sea Surface Salinity Observation Service  (http://www. legos. obs-mip. fr/observations/sss/). The fCO 2 data presented here are available in the SOCAT v3 database (www. socat. info).
Primers were designed using Primer3 software (The Whitehead Institute, http:// frodo. wi. mit. edu/cgi-bin/primer3/primer3_www. cgi) as per the published Epinephelus species SOD, CAT, GR, GST, GPx and b-actin genes sequences (AY035854. 1, AY735009. 1, JF430677. 1, HQ441085. 1, AY510710. 2) of the NCBI database. All primers were provided by Sigma Aldrich (Sigma-Aldrich Chemie GmbH, Steinheim, Germany) and are listed in (Table  1 ). AbiPrism 7300 (Applied Biosystems, Foster City, CA, USA) was used for performing the real-time qPCR reaction. The reaction program was a 94 °C/2 min enzyme activation, then 40 cycles of 95 °C/15 s denaturation, 60 °C/30 s annealing and 72 °C/30 s. The produced fluorescent product was detected at the end of the 72 °C extension period. For each sample, the threshold cycle (Ct) values were used for the determination of mRNA concentration.
Statistical analysis SPSS version 20. Statistical packages (IBM, New York, NY, USA) were used for statistical analysis of the obtained data, that were presented as a mean ± SD, n = 10. One-way analysis of variance (ANOVA) was used to determine the statistical differences between the groups flowed by Duncan's multiple range test to analyze the inter-grouping homogeneity. Data were presented as mean ± S. D. (n = 10) for each animal group. P < 0. 05 were statistical significant.
The French Common Strategic Analysis (FCSA) on climate services: Outlines The economic system of climate services can be divided into several components (Fig. 1 ). First, ''Data providers" deliver the fundamental observations and modeling results allowing the evaluation of past, present, and future changes. These datasets can be used directly by public authorities to raise awareness on climate change impacts and to favor adaptation. Between beneficiaries and data-providers, the products delivered by in-sector providers can be sorted in three categories: (1) organizations designing added value products such as portals and tools providing impact assessment results (e. g. ,  Lémond et al. , 2011; Kjellström et al. , 2016) ; (2) design of adaptation strategies and support for decision making on adaptation (e.
Demonstrators of climate services applied to specific sectors or territories are needed to bridge this gap between research and applications. -Developing common climate services portals, building on the existing ones, so that potential users are supported in their efforts to integrate climate data in their practices. Here, research in the field of geographic information management is needed to ensure that the different types of databases (climate, soils, impacts. ) can communicate and be used in distributed systems of systems. -Continuing the integration of adaptation in the regulation and in public and private tenders so that private actors can take ownership of the challenge of climate adaptation, and translate their investments in this field by an improved offer and competitiveness when responding to such tenders.
Acknowledgement This study was funded by  Fonds National de Valorisation  from  Programme Investissements d'Avenir (French government fund). Gonéri Le Cozannet  is supported by the  BRGM-funded project ''Climate Change Risk and Adaptation ". We are grateful to the 68 persons who responded to the interviews. We thank  Christine King ,  Caroline Chung , and the  Allenvi Climate Working Group and Climate Services committee  for support to the initiative. We thank two anonymous reviewers for their insightful comments, which led to improving the paper.
Methodology Approach Pretreatment of Experimental Data at the Event Scale Every event that occurred between 1 Sept. 2005 and 31 Aug. 2012 with a rainfall amount >2 mm and separated by at least 1 h was considered, but only those that have generated runoff at least at one monitored station were included in the database. For some rare events, both water level and sediment concentration values were missing because of battery failures. For a few other events, only sediment concentration values were missing because the water level didn't reach the minimum sampling water level or because of automatic water sampler malfunctioning. A pretreatment was proposed to identify and reconstruct the missing values. For missing runoff data, the inter-station relationships (Fig. 3a ; Table  1 ) were used because the rainfall-runoff relationships didn't provide satisfactory prediction capacities.
The inter-station relationship with the best determination coefficient was finally used: ʶ P1 vs. MC relationship for missing runoff at P1. ʶ P2+G vs. MC relationship for missing runoff at P2+G or MC. For the missing erosion values, the runoff parameters measured at the same station were used each time they were available to limit the impact of a possible shift in response between stations. In other cases, inter-station relationships (Fig. 3b ) were used. All of these relationships (Table  1 ) showed good prediction performance for the three stations. Depending on the considered station, 6 to 11% of the total runoff amount and 20 to 27% of the total erosion amount were reconstructed. The limited contributions of the reconstructed values and the strong prediction ability of the relationships used to evaluate missing values enabled us to elaborate on a reconstructed database that can be used with acceptable confidence.