dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the conditional bias of the ensemble forecasts (not shown) revealed that this underestimation is larger for weak gusts (< 5 m s -1 ). Still, the application of this dataset for storm-damage modelling would lead to an underestimation of the estimated storm damage probabilities. The second thing to note in Fig. 2  (left panel) is the underdispersion demonstrated by the u-shape. As described in Sect. 3. 5, this indicates an underestimation of the uncertainty on forecasted wind gusts. With increasing forecast lead time, both u-shape as well as the asymmetry in the Talagrand diagram decreases (Fig. 2 , left panel). This means that both underdispersion and frequency bias decrease with increasing forecast lead time, which might relate to the fact that the ECMWF-EPS system, as mentioned previously, is primarily designed for forecasts in the medium range.
Another uncertainty in the relation between gust wind and damage probability arises from the fact that from a data point of view, hail-induced damages cannot be distinguished from wind-related damages in the dataset we use. According to the provider of the dataset (GDV), winter months are dominated by windstorm damages while summer is dominated by hail-induced damages. However in rare cases of severe winter storm events, hail damages may occur. For example, it is known that damaging hail occurred during the frontal passage of storm Kyrill in 2007  (Fink et al. , 2009). Taking into account the occurrence of hail and resulting damages could be done based on additional predictors such as the "convective available potential energy" (CAPE) and "convective inhibition" (CIN). Based on a logistic regression model with multiple predictors, both the individual effect of hail but also the contribution of hail in the case of winter storms could be quantified.
Acknowledgements. This research was carried out in the  Hans Ertel Centre for Weather Research. This research network of universities, research institutes and the Deutscher Wetterdienst is funded by the  BMVI (Federal Ministry of Transport and Digital Infrastructures). Furthermore, contributions to this work have been funded by the  Federal Ministry of Education and Research in Germany (BMBF)  through the research program MiKlip (FKZ:  01LP1104A ) and by  Munich Re. We wish to thank the  Gesamtverband der Deutschen Versicherungswirtschaft e. V. (GDV)  for providing the loss data. We are also grateful to the  German Weather Service (DWD)  and the  ECMWF  for providing access to the EPS data. We would like to thank the editor  Sven Fuchs  and the two anonymous reviewers, whose constructive comments helped to improve the article.
The software used was IMAGEJ  [21]  and FracLac v. 1. 2 [22]. The fractal dimension D of the patterns ranged from 1. 77 for the lowest complexity to 1. 83 for the highest complexity (Fig  1 ). When structure was absent, we gave this a fractal dimension of zero. We measured temperature at the beginning of the experiment and after 24 h (period of leaf leaching) to check that the structures were not warming the microcosms due to their opacity (i. e. confounding complexity with temperature), and indeed this was not the case.
Adjusting for metabolic capacity in the microcosms All animals were photographed at 100X before they were put into the microcosms. The body length of each individual was then measured with the image analysis software Image-Pro1 Plus (Media Cybernetics, Inc. ). The body mass (M) of all crustaceans (in mg dry mass for Asellus and Gammarus and μg dry mass for Cyclops) was estimated from their body length (L) using published equations (Asellus and Gammarus equations after Reiss and colleagues  [13]  and Cyclops after Dumont and colleagues  [23] : (equations given as in the publication). Asellus Instead of adjusting for abundance or biomass in the microcosms, we decided to adjust for the metabolic capacity of each species in the treatments, because metabolic capacity drives feeding activity and, for Gammarus and Asellus, we found previously that decomposition followed a ¾ scaling law with body mass  [14]  which is consistent with the assumption that metabolic power explains decomposition.
We calculated metabolic capacity (MC; in mg) of each monoculture after Perkins and colleagues  [15] , based upon general allometric body mass- Experimental design of the experiment addressing the effect of complexity level and species combinations on three response variables. In total, there were five complexity levels including structure absent and levels 1-4 (created with plastic plants); and 7 assemblage identities (A is Asellus, G is Gammarus and C is Cyclops). Our design was not fully factorial: only monocultures were run with structure absent. We therefore divided the dataset in two sub-sets and used two different collections of statistical models on them (collection A and B). doi:10. 1371/journal. pone. 0165065. g002 metabolism relationships: MC ¼ X ðM 0:75 Þ where M is individual dry mass (mg) of each individual.
5. 1  [29]. The R output is straightforward for Collections A and B. Only collection B needs a little additional computation, because the R output gives the sums of squares for x 1 and x 2 separately: for the final ANOVA table, the SS values x 1 and x 2 must be added to obtain SS for the model `Type'. This must also be done for models that include an interaction between Type and another predictor (see S1 File for R code and worked example).
Complexity measured as 'amount' and 'fractal dimension' in mono-and polycultures We used data from 28 treatments to test for effects caused by species interactions (i. e. monovs. polycultures) and habitat complexity (measured as amount of structure and fractal dimension). Habitat complexity only significantly affected pH (Table  2 , Collection B) and this effect was fully explained by the amount of structure added (2 or 3 plastic rings). The fractal dimension of the habitat was not a significant predictor, because the model 'Fractal dimension' did not explain more than the model ' Amount' in this collection of statistical models (Table  2 ). Average pH was lower in microcosms that had received two plastic plant rings compared to those with three plastic plant rings (data not shown), which might indicate a higher respiration rate at these habitat complexity levels.
Data All fish contamination data collected since 2008 in different rivers from the 6 water bodies in metropolitan areas (Artois-Picardie, Rhin-Meuse, Rhône-Méditerannée-Corse, Seine-Normandie, Loire-Bretagne and Adour-Garonne) were used to generate a database containing more than 5,000 fish PCB contamination values after database cleaning. The contamination values of fish from the parts of rivers studied to set the regression model were aggregated with those from the different surveillance plans and specific PCB plans performed from 2005 to 2013. Altogether, 824 contamination values were available over the 23 parts of rivers previously studied; 228 values from fish with high PCB accumulation potential were available and 596 from fish with low PCB accumulation potential  (Desvignes et al. , 2015).
, 2015) , 2 servings per week (this frequency corresponded to the recommendation for the general population based on a benefit-risk approach, ANSES, 2010), and 1 serving per week (this last frequency was arbitrarily selected between the first two). These scenarios were carried out in taking account of the contamination level cluster described previously. Statistical analyses were performed using SAS 9. 2 software (SAS Institute Inc. , Cary, NC, USA); R 2. 12 software (R Core Team, 2010) and Stata v10 software  (StataCorp, 2007).
Fish contamination database Concentration data from the 3 categories of fish were available (fish with low PCB accumulation potential, fish with high PCB accumulation potential, and eels). Focusing on the population of anglers and their family and associated areas (in total, 23 zones representing 900 km across France) studied in the context of the previously published study  (Desvignes et al. , 2015) , three clusters were created depending on fish contamination according to the Ward approach. The contamination data of fish with low PCB accumulation potential were used to perform the hierarchy since this category is the most represented amongst the three groups of fish. Within each of the clusters, the descriptive statistics of PCB concentrations of fish with high PCB accumulation potential are shown in Table  2.
The mass concentration of PM 2. 5 for the air quality classification and concentrations of pollutant gases (SO 2 , NO 2 , O 3 , and CO) were monitored by the National Environmental Bureau and the data at Huairou were retrieved from the Internet (http://113. 108. 142. 147:20035/emcpublish/). In this work, the PM 2. 5 pollution level was divided into three categories according to the technical regulation on Am-bient Air Quality Index (National Environmental Protection Standard of the People's Republic of China, HJ 633-2012) (http://kjs. mep. gov. cn/hjbhbz/bzwb/dqhjbh/ jcg_fbz/201203/W020120410332725219541. pdf; A. Zhang et al. , 2013; G. J. Zheng et al. , 2015) : clear day (PM 2. 5 concentration ≤ 35 µg m -3 , when the corresponding individual air quality index (IAQI) of 24 h averaged PM 2.
Acknowledgements. The authors wish to thank  Zhouqing Xie  at the  University of Science and Technology of China  for providing the meteorological data used in this paper and  Jun Tao  and  Zejian Lin  at the  South China Institute of Environmental Science  for helpful discussion. This research was supported by the  National Natural Science Foundation of China  ( 41330424 ), the  Natural Science Foundation of Anhui Province  ( 1508085J03 ), the  Key Research Program   of the Chinese Academy of Sciences  ( KJZD-EW-TZ-G06-01 ), and the  China Special Fund for Meteorological Research in the Public Interest  ( GYHY201406039 ). The support of the  CaPPA  project (" Chemical and Physical Properties of the Atmosphere "), funded by the  French National Research Agency  through the " Programme d'Investissement d'Avenir"  (under contract  ANR-10-LABX-005 ), is acknowledged.
Conditioning is based on the residual substitution kriging approach and MCMC sampling (further details can be found in  [12] ). In summary, the parameters used by the generator are: the wind velocity and direction of the event, the space-time variogram model, the average percentage of zero rain, the direction and velocity of rain cells, the mean and standard deviation of the precipitation data, and the rain data at gauged locations for the conditioning. In nowcasting, the parameters of the generator are estimated considering the rainfall data available at the time of the start of the nowcasting. The process has to start after a minimum of hours after the start of an event. Here, we consider 4 hours for this initialization. The rainfall nowcasts are then issued for the next six hours, with a total of 30 ensemble members generated at each hourly time step of the event.
Evaluation We compared the rain fields generated with the SAMPO TBM method with an independent rainfall dataset provided by Météo-France, which is a merge of radar imagery and rain gauges measurements (ANTILOPE product). All events were considered together to calculate statistical scores over all the time steps available. The rainfall nowcasts were evaluated for each lead time, either considering each rain pixel of the study area (maps of scores) or areal rainfalls over selected catchments. The accuracy of the ensemble nowcasts was evaluated considering the ensemble mean and the Mean Absolute Error (MAE). 0$( YDOXHV UDQJH IURP WR DQG DUH negatively-oriented scores, i. e. , lower values are better. We also computed the Critical Success Index (CSI), the Probability of detection (POD) and the Success Ratio (SR), for which we considered a probability threshold of at least 20% of the ensemble members exceeding a precipitation threshold of 1 mm/h.
For these criteria, the value of 1 represents a "perfect prediction", where there are no false alarms or missed alerts  [9]. Scores maps were generated for these criteria. We also calculated the Relative Operating Characteristic (ROC). It measures the ability of predictions to discriminate between events and non-events. It is widely used in the evaluation of probabilistic forecasts and is considered as a measure of potential usefulness. It is characterized by the graphical representation of the probability of detection (POD) against the probability of false detection (POFD), using different predictive probability thresholds (eg, at least 5%, 25%, 50%, etc. of ensemble members exceeding a given threshold). The area under the ROC (AUC) can be computed to estimate the quality of the forecast datasets.
Case-study and data The methodology was applied to the Var ³'pSDUWHPHQW´ in the French Mediterranean region. It is indicated in Fig. 2 , together with the rain gauge network and the studied catchments. The data used to generate precipitation ensembles come from 30 rain gauges and the PANTHERE weather radar data, which is the French operational radar data that provides quantitative precipitation fields in real time. It consists of 5-min precipitation fields with a 1 × 1 km² grid resolution. To evaluate the rainfall nowcasts, we compared them against the ANTILOPE radar data. This is a hybrid product, coming from the PANTHERE data and a rain gauge interpolation (kriging with external drift). All meteorological data were provided by the French meteorological service (Météo-France). In our study, these data were aggregated at the hourly time step.
The data span from October 2009 to March 2013, with the presence of some missing data values at the rain gauge locations. A total of 22 catchments of different area sizes were selected for this study (Fig. 2 ). These catchments were used to calculate the catchment area rainfall (observed and nowcast rainfall). From the data available, we selected 14 events. Each event was considered to start (or to end) when at least one (or no) hourly radar pixel exceeded 5 mm/h anywhere in the study region. Only the most significant events in terms of rainfall accumulation and spatial extension were considered. The selected events have the following characteristics: The duration of the events range from 8 to 34 hours.
Parameters of the rainfall generator Parameters of the TBM algorithm were evaluated separately for each rain event and considering the last observed hourly time steps. They were used to generate the rainfall nowcasts. Hourly weather radar data were used to fit an exponential model to the hourly empirical space±time variograms. The spatial and temporal variogram of the precipitation intermittency has a range between 21±77 km in space and 2± 5 h in time. The nonzero precipitation variograms present a spatial range between 13±89 km and a temporal range between 1±7 h. The average percentage of zero precipitation, varies between 5% and 51% among the studied events. For each event, the wind velocity and the direction were estimated. Values vary between 2. 5 m/s and 13 m/s, and the majority of the events show wind direction from southeast to northwest.
We also observe that MAE values are higher in the eastern part of the study area. This is due to the fact that this is the rainiest area in the region, with several events in our dataset registering high rain values over this area. Figure  4  presents the CSI, POD and SR maps. We can see that the scores are better at the first hours ahead and the nowcasts tend to lose performance as lead time increases. Also here, in general, the nowcasts obtained with the rainfall generator show better performance than the persistence method, notably after the first hour ahead.
The population sample studied represented roughly 1% of the population of the three neighborhoods studied. The survey questionnaires included the following six criteria: Household/home typology(standard of living), level of household awareness concerning water quality and the environment, degree of accessibility to water and sanitation, purchase price for 1 m 3 of water, water contamination and diseases related to contamination or sanitation, and the households' priorities. The surveys were conducted using the "face to face" method with representatives of each household, and the data was processed and the results managed using SPHINX statistical software.
Data Data came from sources with a population wide coverage in which mortality could be related to indicators of socioeconomic position as reported in a census. Web tables A1 and A2 give an overview of the main characteristics of these data sources. For this analysis, we selected all European countries for which data on socioeconomic inequalities in mortality were available for the approximate period between years 1990 and 2010. Data for Spain and Italy came from regional populations (Barcelona and Turin, respectively). Data for England and Wales were only available together. Most data stem from a longitudinal mortality follow-up after a census, in which socioeconomic information of the population at risk and of the deceased has been recorded in the census. Spain (Barcelona) had so-called repeated cross sectional data in which socioeconomic information on the population at risk came from repeated censuses, and information on people who had died came from death certificates.
between the low and high educated was calculated as: (asMr 2004-09, low -asMr 1990-04, low ) -(asMr 2004-09, high -asMr 1990-04, high ) (where asMr=age standardised mortality rate). road traffic accident data were not available for scotland and slovenia Over the two decades of this study, there has been a strong decline in the size of the lower education and occupational class groups. Because a smaller size may imply a more extreme social position-which could in itself lead to a wider gap in mortality-we also studied relative and slope indices of inequality that make adjustments for such changes. 8 Patterns of changes were similar to those reported in this paper, with absolute inequalities in mortality narrowing in many countries among men, and relative inequalities widening almost everywhere, among both men and women.
FFiveFig 4 | 4 Fig 4 | Differences between the low educated and the high educated in absolute mortality change for (a) men and (b) women, between 1990-94 and 2004-09, by country and for five specific causes of death. Difference in mortality change (in deaths per 100 000 person years) between the low and high educated was calculated as: (asMr 2004-09, low -asMr 1990-04, low ) -(asMr 2004-09, high -asMr 1990-04, high ) (where asMr=age standardised mortality rate). road traffic accident data were not available for scotland and slovenia.
Indeed, it was the PALS data set that was used here. We've indicated that in the figure caption.  L393-394: How is the choice to "represent the ensemble spread efficiently and reliably" going to be made? Evans et al (  2013 )? Global temperature trend? Could be controversial! We are aware of the controversy but have not yet made a decision on how this choice will be made. The reference to Evans et al is added for inspiration.  L501-509: this seems a little vague -are periods for extremes analysis part of LS3MIP or not? If so, which periods, why? At this point in time it is very difficult to be more specific: early results should give inspiration to zooming in on particular episodes.  Other minor text suggestions and citations have been included as suggested. This comment is also made by Paul Dirmeyer and Gab Abramowitz.
Acknowledgments For their roles in producing, coordinating, and making available CMIP5 model output, we acknowledge the climate modeling groups (Table  S1 ), the  World Climate Research Programme 's Working Group on Coupled Modelling and the  Global Organization for Earth System Science Portals. This work was supported by the  Australian Research Council (ARC)  including the  ARC Centre of Excellence in Climate System Science  and the  NCI National Facility, Canberra. E. v. S. was supported by the  ARC  under grant  DE130101336. S. M. was supported by the  ARC  under grant  DE130100663. We would also like to thank the two anonymous reviewers for their useful feedback.
Climate change and deforestation data sets To assess the effect of future climate change, projections of five GCMs (see also  Jupp et al. , 2010; Randall et al. , 2007) , using three SRES scenarios (A1B, A2, B1)  (Nakićenović et al. , 2000) , have been applied (Fig. 1a ). The GCMs -namely, MIUB-ECHO-G, MPI-ECHAM5, MRI-CGCM2. 3. 2a, NCAR-CCSM3. 0, and UKMO-HadCM3cover a wide range in terms of temperature and precipitation and have therefore been chosen to account for uncertainty in climate projections. The emission scenario SRES A1B describes a development of very rapid economic growth with convergence among regions, and a balanced future energy source between fossils and non-fossils. SRES A2 describes a development of a very heterogeneous world with slow economic growth. And SRES B1 describes a development of converging world similar to A1B but with more emphasis on service and information economy.
For this scenario the authors assume that the Brazilian environmental legislation is implemented across the Amazon basin and the size of the area under the Protected Areas Program increases. The SimAmazonia scenarios cover the years from 2001 to 2050. After 2050 the frac-  tion of deforested area is kept constant. From 2051 until the end of the century the only driver of change is the continuing climate change. This approach enables us to estimate the consequences of combined dynamics of deforestation and climate change until 2050 and the effects of intensified climate change after 2050, when deforestation is halted at its maximum. Deforestation rates preceding the scenarios (before 2001) were derived from extrapolating the data into the past. LPJmL requires historic land-cover information to correctly capture transient carbon dynamics.
Acknowledgements. We thank the  "Pakt für Forschung der Leibniz-Gemeinschaft " for funding the  TRACES  project for  F. Langerwisch. A. Rammig  was funded by  FP7 AMAZALERT (Project ID 282664 ) and  Helmholtz Alliance  " Remote Sensing and Earth System Dynamics ". We also thank  Susanne Rolinski  and  Dieter Gerten  for discussing the hydrological aspects. We thank  Alice Boit  for fruitful comments on the manuscript. Additionally we thank our LPJmL and ECOSTAB colleagues at  PIK  for helpful comments on the design of the study and the manuscript. We also thank the anonymous reviewers and the handling editor whose comments and suggestions greatly improved the manuscript. Edited by: C. Reick Reviewed by: two anonymous referees.
a b s t r a c t Marine protected areas (MPA) and in particular no-take marine reserves have been identified as important tools for the conservation of reef fish and habitats. A significant challenge of reef fish monitoring is to determine the influence of temporal factors on fish counts. Fish assemblages are dynamic and changes in activity patterns throughout the day can influence the results of surveys. While many monitoring programs account for the impacts of spatial heterogeneity on fish assemblages the effects of short-term temporal variation are less well known. In the present study, we analysed data from 197 video drops inside and outside New Caledonian MPAs and examined temporal variations in various metrics commonly used to monitor marine reserves. In addition to describing short-term temporal patterns related to time of day, tide height and state, and lunar cycle; we also examined the influence of these temporal factors on the size and direction of any MPA effects detected.