dataesr/scientific-paragraphs-categorization huggingface dataset:
Juvenile green turtles were caught, measured, and weighed over 6 years (2011-2012; 2015-2018)  at six bays to the west of Martinique Island (Lesser Antilles). Using different datasets from this global database, we were able to show that the BM of individuals can be predicted from body measurements with an error of less than 2%. We built several datasets including different morphological and time-location information to test the accuracy of the mass prediction. We show a yearly and north-south pattern for the relationship between BM and body measurements. The year effect for the relationship of BM and size is strongly correlated with net primary production but not with sea surface temperature or cyclonic events. We also found that if the bay locations and year effects were removed from the analysis, the mass prediction degraded slightly but was still less than 3% on average.
Model for BM estimation with dataset A A total of 181 captures are considered in dataset A as the circumference was only measured in 2016, 2017 and 2018. The selected model to explain BM included CCL, CCCW, CmidCCL, the interactions between CCL, CCCW and CmidCCL, as well as the identity of the animal, year and location (Table  2 ). Its probability in being the best model among those tested was 0. 45 according to the Akaike weight (Table  2 ). For example, a turtle being measured weighed 50 kg and based on its measurements it would be predicted to weigh between 49. 15 and 50. 85 kg with maximum and minimum being 45. 3 and 54. 6 kg, respectively. With this model, the average error for BM prediction was 1. 70% (range=0%-9. 22%) (Table  3 ). Using this dataset, a significant effect for the location of the bay where the turtles were caught was observed.
When the six bay locations were ordered from south to north, a clear pattern emerged: turtles were lighter relative to their size in the northern bays (w-value=0. 94; not shown for dataset A; see results for dataset B and Fig. 1  for a similar effect). The w-value is the posterior probability that a model with a slope different from 0 is better than a model with a slope fixed to 0 based on the Bayesian information criterion  (Girondot and Guillon, 2018). 2 ). The second model without location effect had a support of 0. 28. With the selected model, the average error for BM prediction was 2. 47% (range 0-22. 05%) (Table  3 ). For example, a turtle being measured weighed 50 kg and based on its measurements it would be predicted to weigh between 48. 7 kg and 51. 2 kg with maximum and minimum being 38.
97 and 61. 0 kg, respectively. Using this dataset, we observed the same significant effect of that of the location of the bay where the turtles were caught as seen in dataset A. When the bay locations were ordered from south to north, a clear pattern emerged; turtles were lighter relative to their size in the northern bays (linear model weighted by the inverse of quasi-standard error at each location, w-value=0. 997; Fig. 1 ). An effect of year was also noticed, and turtles caught in 2011 and 2012 were significantly lighter relative to their size than those caught after 2014 (Fig. 3 ). This effect can also be seen in the pattern linking BM, CCL and CCCW according to the year of capture (Fig. 4 ).
Model for BM estimation with dataset C In this dataset, we considered the same turtles as in dataset B, although the selected model only included CCL, CCCW and the identity of animal. Its probability in being the best model among those tested was close to 1 according to the Akaike weight (Table  2 ). With this model the average error for BM prediction was 2. 39% (range 0-19. 64%) (Table  3 ). For example, a turtle being measured weighed 50 kg and based on its measurements it would be predicted to weigh between 48. 8 kg and 51. 1 kg with maximum and minimum being 40. 18 and 59. 82 kg, respectively. Aside from the detection of significant year and location effects (see results for dataset A and B), the prediction of BM with or without these effects was similar. Model for BM estimation with dataset D The selected model for dataset D included only CCL and CCCW (Table  2 ).
With this model, the average error for BM prediction was 7. 09% (range 0-72. 44%) (Table  3 ). For example, a turtle being measured weighed 50 kg and based on its measurements it would be predicted to weigh between 46. 4 kg and 53. 5 kg with maximum and minimum being 13. 7 and 86. 2 kg, respectively. The confidence interval for the relationship between BM and CCL for young juveniles in Martinique was compatible with the one observed for adults in Ascension Island  (Hays et al. , 2002)  (Fig. 5A ). Similarly, the fitted relationship between BM and SCL for juvenile green turtles in the Bahamas  (Bjorndal and Bolten, 1988, 1989 ) was within the confidence interval for the relationship between BM and CCL for young juveniles in Martinique (Fig. 5B ). However, let us recall that a significant effect of year and location was observed in our datasets.
Physical ecosystem characteristics Monthly NPP and SST as well as 12 h WS are shown in Fig. 6. Correlations between year-effect for log BM versus log CCL and CCCW (see the Model for BM estimation with dataset B) was r=0. 97 (P=0. 002) for year-averaged NPP (Fig. 7 ), r=0. 11 (P=0. 83) for year-averaged SST and, r=0. 68 (P=0. 2) for year-maximum wind Table  3. Fitted fixed effects for conditional Akaike information criterionselected models and prediction errors for datasets A to D speed (P=0. 20). A very significant positive effect of net primary production (NPP) was then noticed with heavier turtles observed for years with higher net primary production in the region.
DISCUSSION Our study aimed to validate the use of different body measurements to estimate BM, which can be difficult to obtain in the field, and explore the determinants of the relationship between BM and size in juvenile green turtles. We show that using a combination of CCL, CCCW and CmidCCL and including a location and year effect, we were able to predict the BM of individuals (range 0-9. 22%) with an average error of 1. 70% (dataset A; Table  3 ). If year, location, or CmidCCL were omitted from the model, the average prediction degraded by a factor 2, while the upper range of error increased by a factor 2 (datasets B and C; Table  3 ). Finally, if only CCL was included in the model, the prediction of BM was considerably degraded and could reach a maximum error of 72% (dataset D; Table  3 ).
Data collection We recorded the date, hour, tag number and place of capture for each turtle. The presence of a passive integrated transponder (PIT) was also recorded; in its absence, a PIT (ID-100, TROVAN ® ) was injected into the right tricep. The number was checked using a manual reader (GR250, TROVAN ® ). The health status of each animal was also recorded, particularly in the presence of a visible external tumour. Each animal was measured (see below) with a flexible measuring tape (±0. 1 cm). Only two trained operators (M. B. and D. C. ) measured the animals. Measurement differences were less than 1% for these two operators. We measured CCL (measured between nuchal and supracaudal scutes) and CCCW (measured between left and right costal scutes 2 and 3)  (Bolten, 1999). Bjorndal and Bolten (1989)  OC carapace length (OCCL) is equivalent to our CCL measurement (Fig.
About 10% of individuals were measured and weighed more than once during field work. The electronic crane scale was suspended from a beam. A hammock, used to hold the turtles during weighing, was attached at the electronic crane scale which was then tared to '0' before each turtle's weighing. Bjorndal and Bolten (1988)  estimated juvenile green turtle BM in the Bahamas based on SL measurements. Bjorndal and Bolten (1989)  also gave equations to convert OC into SL measurements for both the carapace length and width of juvenile green turtles. We used these equations to compare their estimates of BM with our own. B ) Bahamas ( juveniles) against Martinique young juvenile green turtles. In B, the two models are very similar, so the curves are superimposed. Data from Ascension and Bahamas were digitized from original publications using WebPlotDigitizer  (Rohatgi, 2019).
Statistical analyses Statistical analyses were carried out using R software version 3. 6. 1 (R Core Team 2019). For this study, no wounded (fin cut), or sick individuals ( presence of fibropapilloma tumours) were integrated in the analysis. To test the effect of some morphological and time-location parameters on the accuracy of the mass prediction, four different datasets were built: (A) a dataset with BM, CCCW, CCL, CmidCCL, year, location and the identity of the animal; (B) a dataset with BM, CCCW, CCL, year, location and the identity of the animal; (C) a dataset with BM, CCCW, CCL and the identity of the animal; (D) a dataset with BM and CCL. Dataset B allowed us to test the precision of the BM estimation without CmidCCL as it can be difficult to measure and was only recorded in one-third of captures.
Dataset C was a minimal dataset in case the location and year were not be available. Finally, dataset D was used to compare our data with other published analyses for this species  (Bjorndal and Bolten, 1988, 1989; Hays et al. , 2002). For this former analysis, when an individual was measured and weighed on several occasions, only the first measurement was used. Data were then analysed using a linear model without random individual effect so as to have similar conditions as previous studies. Only the individuals with a complete set of information within a dataset were retained for analyses. All measurements were log-transformed to limit the effect of heteroskedasticity. Year was always treated as a categorical variable. About 10% of individuals were measured and weighed more than once during field work.
Physical ecosystem characteristics To link year effect with physical oceanography, ocean net primary production (NPP), sea surface temperatures (SST) and wind speed (WS) were obtained from public databases for the location closest to the capture bays. NPP is commonly modelled as a function of chlorophyll concentration and is based on the original description of the vertically generalised production model (VGPM)  (Behrenfeld and Falkowski, 1997) , MODIS surface chlorophyll concentrations (Chlsat), MODIS 4-micron sea surface temperature data (SST4), and MODIS cloud-corrected incident daily photosynthetically active radiation (PAR). Euphotic depths were calculated from Chlsat following  Morel and Berthon (1989). NPP was compiled from the Ocean Productivity website (http://www. science. oregonstate.
edu). SST and WS were obtained from the European Centre for Medium-Range Weather Forecasts database (https:// www. ecmwf. int). WS was calculated from the two orthogonal WS vectors u and v using WS ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi u 2 þ v 2 p. plongeé, Plongeé-Passion, College Cassien Sainte-Claire and College Petit Manoir for their technical support and field assistance. We are also grateful to the numerous volunteers and free divers for their participation in the field operations. The authors acknowledge the support of the Virtual Data initiative run by LABEX P2IO and supported by UniversitéParis-Sud for providing computing resources on its cloud.
Acknowledgements The authors would like to thank the  DEAL Martinique, ODE Martinique, ONCFS Martinique, ONEMA Martinique, SMPE Martinique, ONF Martinique ,  PNR Martinique ,  Surfrider Foundation ,  CREOCEAN, Carbet des Sciences, Aliotis infrastructure. We thank  Heinz Tuechler  (tuechler@gmx. at) for introducing us to quasi-variance methodology in an r-help discussion (r-help@r-project. org). Finally, we thank  Dr Victoria Grace  (www. english-publications. com)for her careful reading of the manuscript and correction of the English.
00 0. 51 L W Y -819. 26 1. 18 0. 28 L W B -818. 59 1. 84 0. 20 W Y B -769. 04 51. 39 0. 00 L Y B -788. 57 31. 87 0. 00 Dataset C: L W; n=350 L W L:W -823. 00 0. 37 0. 45 L W -824. 01 0. 00 0. 54 Second round L W -824. 01 0. 00 1. 00 L -803. 52 20. 49 0. 00 W -756. 43 67. 58 0. 00 L, W and C are CCL, CCCW, and CmidCCL, respectively. Y and B are years and bay locations, respectively; both are treated as categorical factors with the number of levels indicated in parentheses. n represents the number of captures available for each dataset. Selected models are indicated in bold.
Agradecimientos A Rafael Lira Saade (FES-Iztacala, UNAM), a dos revisores anónimos y a Lourdes Rico Arce por la lectura cuidadosa, detallada y crítica del manuscrito, sus valiosas sugerencias y correcciones; al programa PAPIIT IN-225010 y al PASPA DGAPA-UNAM, por el apoyo para desarrollar el proyecto "Taxonomía, distribución y conservación de Dioscorea en México" y para revisar las colecciones depositadas en los herbarios de Roma (RM), Florencia (FI) y Turín (TO) durante junio de 2017, respectivamente. Al Institut de Recherche pour le Developemment, Francia y a la FES-Iztacala, UNAM por el apoyo para revisar las colecciones depositadas en los herbarios CAY y P; y a los curadores de estas colecciones y de ENCB, K, MEXU, MO, U y XAL por las facilidades brindadas para estudiar los ejemplares depositados en ellas.
Valproate is much more expensive than the other AED, and should be subsidized to encourage treatment adherence. Indeed, we have observed that certain PWE switch from one AED to another or may stop treatment because the drugs are too expensive. An important aspect of the clinic is proper documentation of all patient-related data and activities of the clinic. This will be done primarily using reporting forms adapted from the WHO training modules on mental health gap action program  [57]  for this purpose (see Additional file 3). The following parameters should be monitored monthly (or less often if seizure frequency is < 1 per month): Clinical features: anthropometric parameters, physical examination, seizure characteristics, complications, co-morbidities, deaths Treatment outcomes: adherence to AED, seizure frequency, side effects Quality of life: degree of autonomy, socialization, school/work, anxiety/depression Epilepsy-related expenses: cost of AED and care, transport cost to health centre, number of days of work lost Management: AED and dosage, other measures, date for next appointment Epilepsy clinics should equally provide psycho-social support to PWE and their families.
Acknowledgements We thank the numerous health care workers including CDD we were able to work with, and the families of persons with epilepsy we were able to interview in onchocerciasis-endemic regions. Funding RC received funding from the  European Research Council  (grant No. ERCPoC 768815 ) and RI is funded by the  Medical Research Council UK  through a  MRC/DFID African Research Leader Award  (grant No. MR/M025489/1 ).
Convective cell tracking We have developed a tracking algorithm focussed on the tracking of individual deep convective cells in CRM simulations, but flexible enough to be extended to other applications, e. g. simulations of shallow convection or based on geostationary satellite observations using brightness temperature data. The initial tracking of features is performed on the column maximum vertical velocity at each output time step using Python tracking library trackpy  (Allan et al. , 2016). Table  1. Overview of the 52 simulations performed in this study, including the two cases simulated and the different CDNC/CCN values for each of the microphysics schemes. The CDNCs for the SBM simulations are the median values for grid points with a cloud water mixing ratio larger than 10 g kg -1. Bold values of CDNC and CCN show parameters that were set in the simulation setup; the CDNC values for the SBM were diagnosed from the simulation results.
(2005a, b)  These features are then filtered and linked to consistent trajectories. The trajectories are extrapolated to two additional output time steps at the start and at the end to allow for the inclusion of both the initiation of the cell and the decaying later stages of the cell development. Based on these trajectories, a three-dimensional watershedding algorithm, morphology. watershed from the Python image processing package scikit-image (van der  Walt et al. , 2014) , is applied to the total condensed water content field (mass mixing ratio of all hydrometeors) at each output time step to infer the volume of the cloud associated with the tracked updraft. We use a threshold of 1 g kg -3 to define the core cloudy grid points in the simulations. A variation of this threshold by up to an order of magnitude to 0.
Acknowledgements. Max Heikenfeld acknowledges funding from the  NERC Oxford DTP in Environmental Research  ( NE/L002612/1 ). The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) project  BACCHUS  under grant agreement no. 603445  ( Max Heikenfeld ,  Philip Stier  and  Laurent Labbouz ). The authors acknowledge funding from the  European Research Council  project  ACCLAIM  ( Philip Stier  and  Bethan White)  under grant agreement no. 28002  from the  European Union 's  Seventh Framework Programme  (FP7/2007-2013). Philip Stier  and  Max Heikenfeld  acknowledge funding from the  European Research Council  project  RECAP  under the European Union's  Horizon 2020 research and innovation programme  with grant agreement  724602.
Laurent Labbouz  has also been supported by  CNES. Bethan White  has also received funding from the  Australian Government  through the  Australian Research Council. The authors would like to acknowledge the use of the  University of Oxford Advanced Research Computing (ARC)  facility (https://doi. org/10. 5281/zenodo. 22558) in carrying out this work. We want to thank  Hugh Morrison  and  Greg Thompson  for important discussions about the two bulk microphysics schemes used in the study and  Matthew Christensen  for helpful comments on the manuscript. Edited by: Leiming Zhang Reviewed by: three anonymous referees.
Code availability. The WRF model is publicly available at http:// www2. mmm. ucar. edu/wrf/users/ (WRF Community, 2000, last access: 24 February 2019). The code of the modified WRF model with the microphysical pathway diagnostics for the two bulk microphysics schemes and the additional second supercell case is available from the authors on request along with postprocessing code for the process rate analysis in Python. The tracking algorithm applied in this study has been developed into the tracking framework tobac (Tracking and Object-Based Analysis of Clouds) hosted on GitHub at https://github. com/climate-processes/tobac (last access: 24 February 2019). The version of the code used in this paper is available at https://doi. org/10. 5281/zenodo. 2577047 (Heikenfeld, 2019). The tracking makes use of the trackpy library  (Allan et al.
Single-point U-Pb chemical ages for U-bearing minerals were calculated from EMPA data using the iterative procedure of  Bowles (1990). In this procedure, a first age approximation is obtained from the following equation (1): = λ ln ( + 1) (1) where t is the estimated age of mineral closure, and λ 238 is the decay constant of 238 U. The amounts of 206 Pb and 238 U were calculated from the total lead and uranium contents (atomic %). The amount of Pb corresponding to this approximated age is then calculated using: = ℎ × -1" + # × $0. 99276 × -1 + 0. 007196 × + -1, (2) where t (output from equation 1) is the age of mineral closure, λ 232 , λ 235 , and λ 238 are the decay constants of 232 Th, 235 U, and 238 U, respectively, and Pb, Th, and U are the contents in atomic % of these elements. If the calculated lead content from equation 2 is greater than the measured content, the age (t) is then iteratively modified by 10 Ma and then by 1 Ma until the corresponding calculated lead content matches the measured one.
g. the emplacement of the McKenzie dyke swarms at 1. 27 Ga) and with different far-field, continent-wide tectonic events, such as the Mazatzal Orogeny (1. 6-1. 5 Ga), the Berthoud Orogeny (ca. 1. 4 Ga), the East Kootenay orogeny (ca. 1. 35 Ga), the Grenville Orogeny (1. 1 Ga), and the assemblage and break-up of the Rodinia (1-0. 85 Ga)  (Fayek et al. , 2002a; Fayek et al. , 2002b; Alexandre et al. , 2007; Cloutier et al. , 2009; Cloutier et al. , 2011; Kyser and Cuney, 2015; Sheahan et al. , 2016). The two samples analyzed in the present study are from the unconformity-related Cigar Lake U deposit (Athabasca Basin, Canada). They can be considered as relatively simple cases compared to published data on uraninites from unconformity-related U deposits because the measurements demonstrate a two-stage evolution of the uraninites, with their crystallization at 1300 Ma and then their alteration by a meteoric fluid at around 40 Ma (9672-131) and 70 Ma (9672-162) (Fig.
The 206 Pb/ 238 U and 207 Pb/ 235 U isotopic ratios measured in the different zones of the uraninites from 9672-131 and 9672-162 samples depict two quasi-identical Discordia lines (Fig. 5), with upper intercept ages of 1299 ± 4 Ma (102 analyses) and 1308 ± 14 Ma (66 analyses) and lower intercept ages of 38 ± 13 Ma and 72 ± 22 Ma, respectively. None outliers were removed from the initial dataset. The calculated MSWD are 36 and 49 respectively. The two lower intercept ages are very close considering the calculated errors. The freshest areas, marked by the highest concentrations of Pb (EMPA data and map; Fig. 5), have relatively slightly discordant Pb/U ratios (degree of concordance up to 94. 78% for sample 9672-131 and 97. 08% for sample 9672-162, red circles on Fig. 5), indicating that these zones were affected by a relatively limited but distinguishable lead loss.
Acknowledgments This work is part of a PhD thesis carried out at GeoRessources ( CREGU, CNRS, Université de Lorraine ) and supported by  Orano and Orano Canada Inc. We thank  Cameco Corporation  for access to the samples. We are grateful to  Dr. Mostafa Fayek  of the  University of Manitoba  for providing the UO 2 standard needed for δ 18 O measurements. We also thank the team from the  Service Commun de Microscopies Electroniques et de Microanalyses (SCMEM)  and particularly  Olivier Rouer  for the electron microprobe analyses. We thank  Marie-Camille Caumon  ( GeoRessources laboratory ) for Raman spectroscopy analyses. The authors thank Y. Amelin for editorial handling of the manuscript and two anonymous reviewers who have significantly contributed to the improvement of this manuscript.
Pb and 207 Pb/ 206 Pb isotopic compositions and related ages The 206 Pb/ 238 U and 207 Pb/ 235 U isotopic ratios measured in the different zones of the uraninites from 9672-131 and 9672-162 samples depict two quasi-identical Discordia lines (Fig. 5), with upper intercept ages of 1299 ± 4 Ma (102 analyses) and 1308 ± 14 Ma (66 analyses) and lower intercept ages of 38 ± 13 Ma and 72 ± 22 Ma, respectively. None outliers were removed from the initial dataset. The calculated MSWD are 36 and 49 respectively. The two lower intercept ages are very close considering the calculated errors. The freshest areas, marked by the highest concentrations of Pb (EMPA data and map; Fig. 5), have relatively slightly discordant Pb/U ratios (degree of concordance up to 94. 78% for sample 9672-131 and 97. 08% for sample 9672-162, red circles on Fig.
e. , in the Greek and Spanish cases). Cases were documented by the authors on the basis of previous fieldwork and/or secondary sources such as technical papers, published case studies, Ph. D. theses or government reports to which the authors had access to (see Appendix 2 for details for each case). Special attention in the data collection was paid to the stylized characterization of the default local setting, the public interest pursued by government intervention, and the policy instruments through which such intervention materialized. The analysis proceeded in two steps. First, we developed analytical narratives of the cases  (Bates et al. 1998). This involved the identification of the group of local resource users and their interests, the motivation for the government to promote a change in the behaviour of users, and whether this was accomplished.