dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimated zooplankton size spectra are further investigated to depict correlations between total zooplankton biomass and the shape of the size distribution. This allows the large available data set from surveys made with the LOPC to be used for the characterization of the size structure patterns of the zooplankton in the Bay of Biscay. Estimated zooplankton size spectra are further investigated to depict correlations between total zooplankton biomass and the shape of the size distribution. Following this methodological approximation, the spatial and interannual variability of the habitats defined by the size spectra distribution are discussed in terms of energy transfer to upper trophic levels, from ecological and modeling perspectives.
Data from the CTD probe as well as nutrients and chlorophyll a are available online through the SEADATANET portal (www. seadatanet. org). The total numbers of stations with LOPC and/or WP2/Zs data are presented in Table  1. The French PELGAS survey followed directly after the Spanish PELACUS survey aboard the same vessel, both operating similar CTD, LOPC and WP2 net tows. A span of ∼ 2 months separate the start of the survey in the south from the end in the north of the Bay. The distance between stations are approximately 24 nm (∼ 44 km) in the alongshore direction, and 10 to 25 nm (∼ 19 to 46 km) in the cross-shelf direction which shows strongest gradients, with refinement for the latter in the known frontal areas and over the Iberian shelf. This sampling strategy prevents any assessment of observed variability at short temporal  (Sourisseau and Carlotti, 2006)  and spatial scale, yet, interpretation may be affected by population dynamic patterns or patchiness.
Environmental data set Hydrological parameters were measured simultaneously with LOPC records. Values of temperature, salinity and density at surface and bottom were used (surface ∼ 4 m depth, bottom corresponds to 100 m or less, depending on the bathymetry). The total fluorescence integrated from surface to bottom, the depth of the maximal fluorescence and the value of fluorescence at its maximum were also included. Fluorescence data were calibrated each year against in situ measurements of chlorophyll a. The stratification (first differences) was computed from density and the mean and maximum values were used. The mixed layer depth was also included, calculated by a finite difference criteria (0. 02 kg m -2 higher than the surface, 4 m depth, value,  Kara et al. , 2000). In addition, daily satellite data of chlorophyll a and inorganic suspended particulate matter (SPM), processed with a specific algorithm for Case 2 coastal waters  (Gohin et al.
, 2005) , at the sampling date as well as 7 and 14 days before were included. Finally, bathymetry, distance to the coast, latitude and Julian day were also included. This formed a data set of 22 environmental explanatory variables. Integrated and maximum fluorescence, satellite chlorophyll a and suspended matter as well as bathymetry were log transformed. At 68 stations  (PELGAS 2009 (PELGAS , 2010 (PELGAS and 2011)) , the surface chlorophyll a was measured for three size fractions, i. e. , below 3 µm, from 3 to 20 µm and above 20 µm.
The stepwise RDA was also performed on reduced Y tables including variables not available for all stations (e. g. , fractionated chlorophyll a). Then, in order to compute the coefficients of the multiple regression to be applied on the explanatory variables to estimate a nl and b nl , a partial least square regression was used  (PLS regression; de Jong, 1993; Rosipal et al. , 2006). The robustness of the PLS regression was assessed by performing a total of 5000 permutations with a random removal of 4 stations for each permutation. Average PLS regression coefficients were then applied on the whole LOPC size spectra to estimate the zooplankton size distribution for each station of each year occurring in the data set. The methodology of  Petrik et al. (2013)  was also implemented in the analysis.
The production was summed from the surface to a max of 100 m depth to have an estimated productivity per square meter. Maps of estimated zooplankton biomass, slopes and productivity were computed for each year on a 50 × 50 grid (∼ 20×20 km cells) covering the whole Bay of Biscay. Average maps were then generated from the means of each annual map. The interannual and spatial variability of zooplankton NBSS were analyzed through the distribution of five groups that were calculated by K means clustering computed on normalized values of slopes and log-transformed biomass. The number of groups was arbitrarily chosen to produce the best trade-off between the level of details needed to detect patterns and readability. All analyses were performed with Matlab R2012a with the use of the Fathom toolbox for the Stepwise RDA (David L.
Acknowledgements. We are grateful to the  R/V Thalassa  crew, and to  J. Massé  for coordinating the PELGAS surveys over the last decade. We also thank  B. Planque  for initiating the use of the LOPC on PELGAS survey, and to  E. Antajan  for helping us with the use of the ZooScan. We also want to greatly thank  Frédéric Ibañez  who introduced some of us to the world of statistics and sadly passed away recently. P. Vandromme fellowship was founded by the project REPROdUCE of the  ERA-NET MARIFISH (Contract ERAC ,  CT-2006-025989 ) and french  National Research Agency (ANR). The  SEAMAN  project of the  EU FP7 ERA-NET SEASERA  completed the funding. The constructive and helpful reviews of  E. Bachiller  and  S. Basedow  greatly improved the manuscript. The service charges for this open access publication have been covered by a  Research Centre of the Helmholtz Association.
Vandromme et al. : Biscay zooplankton size spectra 823 Both PELGAS and PELACUS cruises are part of the Data Collection Framework (DCF) of the European Commission. Collected data are shared within the Working Group on Horse Aackerel, Sardine and Anchovy (WGHMSA) and the Working Group on Accoustics and Ushant front Brittany FRANCE Vilaine Loire Charente Gironde Cantabrian Sea Cap-Breton Adour Galicia PORTUGAL SPAIN 40 Ocean Sci. , 10, 821-835, 2014 www. ocean-sci. net/10/821/2014/ P.
However, we are not aware of previous investigations of work-related stress and the risk of COPD exacerbations. In this study, we examined the associations between job strain (the most widely studied conceptualisation of work-related stress) and severe COPD exacerbations using individual-level data from 10 prospective cohort studies from the Individual Participant Data Meta-analysis in Working Populations (IPD-Work) Consortium  [7]. Job strain is defined as a combination of high demands (excessive amounts of work) and low control (having little influence on what tasks to do and how to carry them out) at work. We ascertained job strain from the participants' responses to questions on demands and control aspects of their work at study baseline. The responses were scored and for each participant, and mean scores were calculated for job-demand items and job-control items.
The Schoenfeld test suggested the proportional hazards assumption to be valid. Study-specific estimates were pooled in random effects meta-analyses and heterogeneity among these quantified using the I 2 -statistic. All statistical analyses were conducted using Stata 11 (Stata Corporation Ltd, College Station, TX, USA), apart from study-specific analyses in the Danish studies, which were conducted using SAS 9. 2 (SAS Institute Inc. , Cary, NC, USA). Our analyses were based on 92 428 males and females (median age at baseline: 42 years), of whom 451 experienced a COPD exacerbation leading to hospitalisation (n5431) or death (n520) during the follow-up (table  1 ). The median length of follow-up was 10 years, ranging from 2 to 23 years, and the mean¡SD age at first severe COPD exacerbation was 60¡9 years.
Acknowledgements: The authors thank the children who participated and their families, and the  primary school ''Ecole de Brabois'', Vandoeuvre, France. Support statement: The  IPD-Work Consortium  supported by the  European Union New OSH ERA  research programme (funded by the  Finnish Work Environment Fund , the  Swedish Research Council for Working Life and Social Research , and the  Danish National Research Centre  for the Working Environment), the  Academy of Finland  ( 132944 ) and the  BUPA Foundation  ( 22094477 ). M. Kivima  ¨ki is supported by the  UK Medical Research Council  ( K013351 ), the  US National Institutes of Health  ( R01HL036310  and  R01AG034454 ), and a professorial fellowship from the  UK Economic and Social Research Council. Details  of funding for each constituent study are provided on the study websites.
ACKNOWLEDGEMENTS Thanks are due to  J. Radofilao  who has explored and surveyed most Ankarana caves and provided maps and observations. Teams members  F. Tessier ,  J. -Ch. Peyre ,  D. Bessaguet ,  P. Gilli , and  G. Tennevin  for their help during the field trips. C. Fandel  who improved the French to English translation. P. Williams,  B. P. Onac ,  M. Veress ,  L. Piccini , and the other IJS reviewers who considerably helped me to organize and improve this paper.
Acknowledgments: We would like to thank  Céline Labrune , curator of the natural history collection of the  Laboratoire Arago , and  Jean-Michel Amouroux  who gave us the opportunity to meet  Dr Ruth Barnich  at the occasion of the 4 th annual workshop of the French network of marine stations and observatories (RESOMAR). This work is a part of the PhD thesis of Sandrine Fanfard sponsored by the  French Ministère de l'Enseignement Supérieur et de la Recherche.
Acknowledgments We greatly thank  Lucie Schmaltz  and  Christophe Germain  for providing us qualitative information on the flamingo database. We thank the associate editor,  Elena Machetou , and one anonymous reviewer for very helpful comments on an earlier version of the article. This research was supported by a  BIOSTIC project  ' Techniques markoviennes avanc ees en dynamique des populations ' financed by the  Montpellier sud de France University.
Introduction Crop phenological dynamics should be essential for evaluating crop production  [1] , especially in the West African food-insecure regions. Vegetation conditions must be carefully monitored using early warning systems during the critical growth stages when estimating year-end crop yields in these regions. There, millet and sorghum, like other cereals, are cultivated under rainfed conditions. Thus, the timing of the photoperiodic phenological stages for these crops varies from year to year due to variable sowing dates, which are farm-level management decisions that depend on soil moisture and temperature conditions following the onset of rainfall  [2]. Over the last two decades, global remote sensing dataset availability has provided new means for studying global vegetation patterns and dynamics  [3, 4].
Moreover, on a regional scale, vegetation phenology also depends on soil, micro-climates, regional climates, land use and management, for which complex spatio-temporal phenology patterns can be observed  [23]. Thus, remotely sensed vegetation index (VI) data should include the main intra-seasonal vegetation dynamics and integrate both rainfall variability and land cover status. This is the reason why without field observations on a large scale, satellite-derived phenological indicators could be relevant for food security early warning systems, which may indicate risky situations in the region due to delayed crop growth. In this study, the objectives were to (i) qualify MODIS MCD12Q2 product, which increasingly interest the agricultural community and should grow in the future to monitor crops on a regional or global scale and (ii) test whether phenology variables (phenometrics) derived from a MODIS Land Cover Dynamics Yearly (MCD12Q2) product express the spatio-temporal variability of crop phenological stages in Southern Mali.
The MODIS Land Cover Dynamics Product (MCD12Q2) Two MCD12Q2 tiles that cover Mali were downloaded for 2002 to 2008. The yearly MODIS Land Cover Dynamics product (MCD12Q2;  [28] ) was developed to support seasonal phenology and inter-annual variation studies on land surface and ecosystem properties. The Collection 5 land cover dynamics product is described in Ganguly et al. [29]  and available online for 2000 to 2010 (accessible from  [30] ) at a 500-m spatial resolution. This product was generated each year using the eight-day vegetation index EVI (Enhanced Vegetation Index) calculated from the NBAR reflectance (Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Two full years of NBAR EVI observations were assembled using a window with six months of data before and after the 12-month period of interest.
Uncertainties, Data Limitations, and Interpretations For the MCD12Q2 product, we showed in a previous study that the Zhang algorithm is not applicable in instances with greater than two consecutive missing 16-day NBAR EVI values, which is often the case in tropical West African countries  [34]. Thus, artifacts can be introduced into the MODIS Land Cover Dynamics product due to the few opportunities for a clear view of the ground during the rainy season; thus, the data tend to be contaminated with clouds. Accurate results require good cloud screening and removing aerosols from the image. As a result, determining the SMAX and EMAX indicators from the MODIS Land Cover dynamics products was more difficult than determining the SOS and EOS, and we agree with Brown and de Beurs who hypothesized that the different phenometrics performances (especially at the season maximum) would decrease due to cloud residue in the composite data.
As satellite observations are often contaminated by clouds during the maximum of the season in West Africa, this study mainly indicates that the observed SOS can easily be used in early warning systems for crop monitoring. For example, in semi-arid monsoonal ecosystems, where food crops are often photoperiodic, a sowing delay reduces the yield due to growing season length  [48]. Therefore, combined with historical sowing date data, the satellite-derived SOS from the MODIS MCD12Q2 product could be used as an early warning indicator of risky conditions in the region. These data support a previous study from Brown and de Beurs  [20] , wherein existing methods that use sowing date ground observations were assessed, and the same SOS metric derived from multiple sensors and rainfall data was compared; a metric based on NDVI and relative humidity was developed therefrom.
This paper presents a multi-proxy dataset across a set of Dansgaard-Oeschger (D-O) events. A particular novelty is that it presents the first high resolution 17O-excess data across a series of D-O events. The authors attempt to make the case that they can discern different substages within GS9, and they suggest that one of these stages is associated with iceberg discharges. If this were correct, it would allow a clearer definition of how Heinrich Events fit into the DO paradigm. Firstly, I would like to congratulate the authors on producing the 17O and new 18Oatm data that are the basis for the paper: both are impressive datasets. However the way the authors use the data requires a chain of guesswork and circumstantial evidence that is not sufficient to support the bold claims they make, not least in the title of the paper.
However this is no basis for the strong conclusions that follow; on the showing of the only published work we are aware of, the conclusion would actually be the opposite. I don't advocate the opposite conclusion, and other factors have to be taken into account (such as the effect of lower accumulation rates on concentration, of higher dust on MSA preservation, and of changing transport paths and source regions). However, it does mean that the strong interpretation in terms of sea ice is simply not currently supportable. (As an aside I wonder why the authors used GISP2 MSA, when MSA data were certainly collected at NEEM). C242 The second issue is simply with the way the data are described, particularly the 17O data on which the story is hung. The description in the first paragraph of section 3.
THEORETICAL BACKGROUND The standard state adopted in this study is that of unit activity of pure minerals, the plagioclases, and H 2 O at any temperature and pressure. For aqueous species other than H 2 O, the standard state is unit activity of the species in a hypothetical 1 molal solution referenced to infinite dilution at any temperature and pressure. The activity of plagioclase surface species was assumed equal to their mole fraction. All thermodynamic calculations reported in this study were performed using the PHREEQC computer code (Parkhurst and Appelo, 1999) together with its llnl. dat database to which thermodynamic data consistent with phase relations observed in altered basaltic rocks reported by  Gysi and Stefansson (2011)  have been added for magnesite, siderite, thomsonite, scolecite, mesolite, laumontite, heulandite, analcime, Ca-stilbite, Ca-mordenite, Ca-clinoptilolite, Fe-celadonite, antigorite, amorphous SiO 2 , amorphous FeOOH, amorphous Al(OH) 3 , gibbsite, allophane, and imogolite.
The residence time is defined as the volume of the reactor divided by the reactive fluid flow rate. Rates obtained from experiments that adhere to this steady-state criterion are noted in bold font in Tables  4  and 5. Outlet fluid compositions were used to calculate the saturation state of primary and potential secondary mineral phases in all experiments. The results of these calculations for the primary and selected potential secondary minerals are shown in Annex C. The fluid compositions of all experiments were strongly undersaturated with respect to those minerals in the adopted revised llnl database but not included in Annex C. The outlet fluids were calculated to be undersaturated with respect to all primary and potential secondary minerals apart from the fluids at pH 5-9, which were calculated to be supersaturated with respect to the Al-oxyhydroxides gibbsite, boehmite, and diaspore.
The degree to which Eqs. (  13 ) and (  14 ) describe the measured plagioclase steady-state dissolution rates can be assessed with the aid of Fig. 9. The average difference between calculated rates and the 40 rates measured during the experiments run in series is 0. 22 log units. The corresponding difference between calculated and the 31 rates measured in the individual experiments is 0. 5 log units. It is evident that rates obtained from the experiments run in series for approximately 75 h are better described by these equations than those obtained in the longer individual experiments, due to the better internal consistency of the former dataset.
ACKNOWLEDGEMENTS We would like to thank our friends and colleagues, including  Helgi A. Alfredsson ,  Gabrielle J. Stockmann ,  Alexander P. Gysi ,  Iwona Monika Galeczka ,  Kiflom Mesfin ,  Eydı ´s Salome Eirı ´ksdottir ,  Andri Stefansson , and  Niels O ´skarsson at the University of Iceland ,  Thierry Aigouy ,  Sophy Gouy ,  Philippe de Parseval ,  Julien Declercq ,  Jacques Schott ,  Vasileios Mavromatis ,  Quentin Gautier , at the  GET/CNRS  in Toulouse,  Ingvi Gunnarsson ,  Edda Sif Arado  ´ttir and  Bergur Sigfu  ´sson at Reykjavı ´k Energy. Comments by  Helge Hellevang  and  Carl Steefel  aided greatly in manuscript revision. We would like to thank the  Department of Mineral Sciences at the Smithsonian Institution  for anorthite sample, NMNH 122261, used in this study. This study was funded by The  Environmental Fund of Reykjavı ´k Energy  through the  CARB-FIX project , and the  European Community  through  DELTA-MIN (Mechanisms of Mineral Replacement Reactions ; Grant  PITN-GA-2008-215360 ) and  CARB-FIX  ( Collaborative  Project -FP7-469 ENERGY-2011-1-283148 ).
Methods We collated all the methods for measuring evolutionary isolation for creating species-specific conservation priorities from the literature (named and defined in Table  1 )  [11] [12] [13] [14] [15] [16] [17]. To aid simulation work, all metrics were first cast in a common analytical framework (Table  1 ). We then created sets of simulated phylogenies (physim function in R package phytools)  [18]  to test how the scores computed using the different metrics differed on a variety of tree shapes. We created and.
We then examined the relative contribution of each metric to two dimensions of a species' position in its tree: its 'uniqueness' (its pendent edge length) and its 'originality', (measured in this case as the mean patristic distance to all other species). We did this projection in the following way: For each simulated tree (from the 100 tip tree set with l50. 5 and m50. 25), we first created two sets of 11 correlations scores: one from comparing the 11 metrics scores for each tip to each tip's pendant edge value (spearman rank correlation, cor function in R package stats)  [21]  and a second from comparing the 11 metrics scores for each tip to each tip's mean pairwise distance value (spearman rank correlation, cor function in R package stats)  [21]. To prevent double representation of the same evolutionary isolation method, however, correlations for three metrics shown to be identical to others (see below) were subsequently dropped from each data frame [FP, VW & CHR], resulting in two sets of eight correlations per tree.
Relationship among metrics Some pairs of metrics were always very closely related -the pairings of VW/ MVW, ED/SV and APD/CHR (see Table  1  for metric acronym definitions) all had mean distances of 0, 0. 04 and 0. 12 (respectively) on the clustering trees where there is an expected pairwise distance of 1. Conversely, the most different metrics were APD and any members of the group {ES, ED/SV, PE}, which had a mean distance of approximately 1. 56 units across the sets of clustering trees. The clusters emerging from each of the five simulated datasets supported, or at least did not contradict, the following principal groups: {ES, ED, SV, PE}, {NWW, APD, CHR} and {QE, VW, MVW, NWU} (for consensus trees see Fig. 1 ). In general, the latter group most often represented a 'middle ground' between the two other groups.
The Arctic seasonal snowpack can extend at times over a third of the Earth's land surface. This chemically dynamic environment interacts constantly with different environmental compartments such as atmosphere, soil and meltwater, and thus, strongly influences the entire biosphere. However, the microbial community associated with this habitat remains poorly understood. Our objective was to investigate the functional capacities, diversity and dynamics of the microorganisms in snow and to test the hypothesis that their functional signature reflects the snow environment. We applied a metagenomic approach to nine snow samples taken over 2 months during the spring season. Fungi, Bacteroidetes, and Proteobacteria were predominant in metagenomic datasets and changes in community structure were apparent throughout the field season.
The nine samples chosen in this study for metagenomic analyses were representative of eight distinct groups defined by chemical and taxonomical analysis  (Larose et al. , 2013c). Snow chemistry was analyzed as described previously  (Larose et al. , 2010b). Briefly, total Hg was measured with a Tekran Model 2600 using USEPA method 1631 revision E and bioavailable Hg was determined using a mer-lux biosensor at the field laboratory. Samples for methylmercury and chemical analysis were shipped frozen to the laboratory in France where methylmercury was analyzed by purge and cryotrapping gas chromatography and inorganic ions and organic acids were measured by suppressed ion chromatography using a Dionex ICS 300. Chemical data are provided in Table  1.