dataesr/scientific-paragraphs-categorization huggingface dataset:
Environmental variables Since the effects of environmental variables on bat activity can change according to the spatial scale considered and are very contextdependent and difficult to predict, we calculated their metrics regarding average, proportion or length at five different buffer sizes used in previous studies  (200, 400, 600, 800 and 1000 m; Kalda et al. , 2015; Lacoeuilhe et al. , 2016) , using the QGIS software. For each sampling site and each buffer size, we first extracted the average radiance in buffers from VIIRS night-time lights. Then, to characterize landscape composition, we computed 11 land-use variables (either distances or length/proportion in buffers) varying across sites (Table  S5 ), which are well-known good bat activity predictors: the proportion of, and distance to urban (e.
g. Azam et al. , 2016) , forest (e. g. Boughey et al. , 2011) , farmland (e. g. Roeleke et al. , 2016)  and wetland areas (e. g. Amorim et al. , 2018; De Conno et al. , 2018) ; hedgerow length (e. g. Froidevaux et al. , 2017; Lacoeuilhe et al. , 2016) , road (e. g. Berthinussen & Altringham, 2012) and wooded edges  (Heim et al. , 2017). Radiance was extracted from VIIRS night-time lights, which is a yearly and cloud-free composite raster (2016) with a 450-m pixel size produced by the Earth Observation Group and NOAA National Centres for Environmental Information (NCEI) (https://ngdc. noaa. gov/eog/v iirs/download_dnb_composites. html). Environmental variables (2016) were provided by the National Institute of Geography in a vectorial shapefile with a 2. 5-m spatial accuracy (from BD TOPO for data on forests, hedgerows and urban areas, from BD Carthage for wetland data, from BD ORTHO and Graphical Parcel Register for farmland data).
Statistical analyses We examined the radiance effects on bat activity according to landuse variables around sampling sites (i. e. landscape composition). A summary of the statistical analysis steps presented below is shown in Fig. S5. We ran Generalized Linear Mixed Models (GLMMs, R package lme4;  Bates et al. , 2015)  for each species or species group separately, using the number of bat passes per site as response variable. We used a negative binomial error distribution to keep low overdispersion ratios in models (<1. 50; Table  S5 ;  Zuur et al. , 2009). For two rare taxa (Barbastella barbastellus, Rhinolophus spp. ; Table  S3 ), information relative to bat activity variation were too scarce, thus we used the presence-absence per site as response variable associated with a binomial error distribution.
To do this, we ran one univariate model per variable per buffer size for each species, and selected the one with the smallest Akaïke Information Criteria (AIC). Thus, each full model can contain different optimal buffer sizes between variables (Tables  S6  & S7 ). Full models were constructed by excluding any explanatory variable responsible for multicollinearity with a <2 variance inflation factor (VIF) value using the vif function (R package car; Fox and Weisberg, 2019; see Supporting information S4 and table S6 for full models composition). Thus, removing variables responsible for multicollinearity restrained the number of predictors (ranging from 8 to 17) in full models, depending on species (Table  S6 ). We also assessed potential non-linear effects for all variables by visual inspection of plots from Generalized Additive Mixed Models (GAMM, R package mgcv;  Wood, 2011).
Seven quadratic relationships were observed for B. barbastellus, Miniopterus schreibersii, Myotis spp. , Pipistrellus kuhlii, Pipistrellus nathusii and Plecotus spp. To account for these non-linear relationships, we added quadratic effects on these variables in full GLMMs (Table  S6 ). We also checked for potential spatial autocorrelation in model residuals using the Moran. I function to test significance (R package spatial;  Venables and Ripley, 2002). We found significant spatial autocorrelation for Eptesicus serotinus, Myotis spp. , Nyctalus leisleri, Nyctalus noctula and P. pipistrellus. To the full models, we added a distance-weighted variable in addition to other explanatory variables, to account for spatial autocorrelation (Table  S6 ;  Bardos et al. , 2015) , using the autocov_dist function (R package spdep;  Bivand and Wong, 2018).
Then, following a multi-model inference approach  (Grueber et al. , 2011) , we generatedfrom the full model for each species and species groupsa set of candidate models containing all possible variable combinations ranked by corrected AIC (AICc). Using all models with a delta AICc <2 (and a delta AICc <6 for comparison;  Grueber et al. , 2011) , we averaged regression coefficients for each fixed effect, using the model. avg function (R package MuMIn;  Barton, 2015). Fixed effects were averaged only over models in which that fixed effect appeared (i. e. the so-called natural average method;  Grueber et al. , 2011). From the model averaging procedure, we were also able to extract the relative importance of each variable (estimated by summing the weights of models in which the variable appeared) and the percentage of explained variance by full and best models (i.
Acknowledgements This work was supported by the  French land-transport infrastructure, ecosystems and landscapes (ITTECOP) program , including funding sources from the  Environment and Energy Management Agency (ADEME)  and the  Foundation for Research on Biodiversity (FRB) , as well as the  LabEx DRIIHM  ( ANR-11-LABX-0010 ). We especially thank the  Auvergne-Rhône-Alpes region  for funding data collection fees. We are also grateful for  Léa Mariton  help with proofreading.
Agenda The purpose of this study is to provide a regression-based model that will allow to predict annual frequency and severity of subsidence claims to be made, based on market data and climatic indicators. The model created in this study bases future predictions on past occurrences, thus a historical insurance database was necessary to calibrate the models, alongside indicators of the severity of historical events that could be reproduced into the future. These indicators were created using climatic and geological data that capture the specifics of past events and regional information. The creation of this database and the choice of indicators will be described in section 2. Using this historical data, various models are implemented, chosen to adapt to the particularities of the data, in order to improve the precision of the predictions.
And finally, in section 4, we will present some models to predict to total costs of subsidence events, in France, and again, study mode carefully the prediction errors, in 2017 and 2018. Our dataset was aggregated at the town level 1  , with no information about the particularities of each individual contract that could influence the claims (such as number of stories, size, orientation, etc. ), creating the need for additional information about the inherent risks of the town and its climatic and geophysics exposure. One of the main issues when modelling subsidence is the absence of precise temporal and geographical definition of a subsidence events. Thus, to combat these issues, indicators must be created to grasp the geographical and temporal characteristics of past events. In Section 2. 1, after describing briefly the specificity of the French insurance scheme, we will explain the variables of interest we will model afterwards, namely the occurrence of a natural disaster (based on official data in France), the number of houses and building claiming a loss, and the amount of the losses (those last two based on data from three important insurance companies in France, representing about 20% of the French market).
The number of towns that have had their request declined has increased since 2003. Overall, the proportion of acceptance is of 61%, however, just taking years subsequent to 2003, the proportion is of only 50%. As mentioned earlier, this might be explained by the fact that, according to the law, those events should be caused by "the abnormal intensity of a natural agent ". Thus, if a town is claiming losses every years, it ceases to be "abnormal ", and claims might be rejected then. The evolution of the number of natural catastrophes since 1989, which is the year of the oldest natural catastrophe in the dataset, shows that since the 1990s, the number of orders has been quite variable, however, 4 years seem to be abnormally  hardly-hit: 2003, 2005, 2011 and 2018. Note finally that  Wües et al.
Indeed, the data used was the monthly average water content, the monthly average soil temperature and the monthly average daily precipitations at a 9km grid resolution globally, from the ERA-5 Land monthly database (Climate Change Service Climate Data Store (CDS) (2020)), between January 1981 and July 2020. Therefore, only the SSWI and SPI were selected as they only require soil water content and precipitation respectively and are simple to implement. Note that this SSWI, recently discussed in Torelló-Sentelles and Franzke (2021), was inspired by  Hao and AghaKouchak (2014)  and  Farahmand and AghaKouchak (2015). The use of precipitation data alone is the greatest strength of SPI, as it makes it very easy to use and calculate. The ability to calculate over multiple timescales also allows SPI to have a wide scope.
Other spatial explanatory variables The concentration and presence of clay in the topsoil plays an important role in the occurrence of subsidence as it is caused by the shrinkage and swelling of these particular kinds of soil. Thus, to express the risk of an area, a map was obtained from the Land Use and Cover Area Statistical Survey (LUCAS) database published by the European Soil Data Centre (ESDAC)  (2015) , which collects harmonised data about the state of land use and cover over the European Union. This map gives the concentration of clay in the topsoils (soils at 0-20cm depth) and is available at a 500m grid resolution over the European Union. The soil clay concentration was then aggregated by town keeping the highest concentration of clay in the town, which gives the maps in figure  2a.
Other aggregation functions were considered (such as the average), but they were less predictive than the maximum. This map shows that the areas with the highest clay concentrations appear to be in the North-East of France, in Charente-Maritime (West), around Toulouse (South-West) and along the Mediterranean Sea (South-East). These variables were then aggregated to the exposure dataset. Thus, the calibration dataset was composed for each claim year of the "INSEE code" of the town (from the official classification, that can be seen as the ZIP code used in the U. S. ), the year of the claim, the number of claims, the cost of claims (in e), the number of policies, the total sums insured (in e), the clay concentration in the soil, the ESPI, the ESSTI, the ESSWI and Cat (the binary categorical variable giving the occurrence of a historical natural catastrophe declaration request, prior to the year of study).
Cross-Validation method In order to assess the predictive power of the calibrated models, cross-validation was used. The main goal of the models is to predict accurately the number of claims, per town, on a yearly basis, thus a yearly cross-validation, that is derived from the basic cross-validation principle, will be used. The idea of cross validation is related to in-sample against out-of-sample testing. In a nutshell, the model is fitted on a subset of the data, and validation is computed on observations that were left out. This approach is interesting to assess how the results of some statistical analysis will generalize to an 'independent' data set. Note that it is possible to remove one single observation, and to validate the model on that one -this is the leave-one-out approach -or to split the original dataset on k subgroups, to remove one subgroup to fit a model, and predict on that specific subgroup, and rotate -this is the k-fold cross-validation approach.
This can be performed using randomForest R package. -A Poisson Random Forest (RFP) which considers count data, with the aim to better capture the distribution of the data. Poisson Random Forests are a modified version of Breiman's Random Forest allowing the use of count data with different observation exposures. This is done by modifying the splitting criterion so that it maximised the decrease of the Poisson deviance and an offset has been introduced to accommodate for different exposures. This Random Forest was calibrated using the rfPoisson function available in the rfCountData package in R. These Random Forests were all tuned in order to obtain optimal number of trees, number of variables tested at each split and maximum number of nodes. However, the tuning was limited given the length of the tuning process, which may reduce the quality of the models.
This was applied through two main components: the development of new drought indicators using Open Data and the use of parametric and tree-based models to model this risk. Modelling subsidence requires the integration of meteorological and geological indicators to ascertain the factors predisposing a policy to subsidence. Without this information, the inherent risk to which a dwelling is exposed cannot be perceived. For this reason, geological and meteorological data was obtained from Open Data sets. This data was paired with insurance exposure and claim data to obtain a complete dataset used to calibrate the models. However, the data available was only at a communal mesh, making the results less precise. Indeed, subsidence is a very localised risk whose modelling would benefit from policy level data.
Subsidence risk in France and our dataset A yearly claim and exposure dataset, sourced from several different French insurers, relative to Multi-Peril Housing Insurance (Multi-Risque Habitation) for individual houses in metropolitan France, for the period 2001 to 2018, was used here for frequency and severity of past events. This dataset was enriched with additional information based on geophysical indices usually used to model droughts. https://doi. org/10. 5194/nhess-2021-214 Preprint. Discussion started: 3 September 2021 c Author(s) 2021. CC BY 4. 0 License.
Acknowledgements. The authors wish to thank  Hélène Gibello ,  Sonia Guelou ,  Florence Picard  and  Franck Vermet  for comments on a previous version of the work. Arthur Charpentier  received financial support from the  Natural Sciences and Engineering Research Council of Canada  ( NSERC-2019-07077 ) and the  AXA Research Fund. 525 24 https://doi. org/10. 5194/nhess-2021-214Preprint. Discussion started: 3 September 2021 c Author(s)  2021. CC BY 4. 0 License.
2 Subsidence risk in France and our dataset A yearly claim and exposure dataset, sourced from several different French insurers, relative to Multi-Peril Housing Insurance (Multi-Risque Habitation) for individual houses in metropolitan France, for the period 2001 to 2018, was used here for frequency and severity of past events. This dataset was enriched with additional information based on geophysical indices usually used to model droughts. https://doi. org/10. 5194/nhess-2021-214 Preprint. Discussion started: 3 September 2021 c Author(s) 2021. CC BY 4. 0 License.
Methods: We systematically searched PubMed, Embase and Web of Science databases (from inception to 23-April-2022) for papers reporting outcomes in adult male and female after out-of-hospital cardiac arrest. The main study outcome was the rate of adjusted survival to hospital discharge or 30 days. Secondary outcomes included unadjusted survival to hospital discharge and favourable neurological outcome. Results: A total of 28 studies were included, involving 1,931,123 patients. Female were older than male, their cardiac arrests were less likely to be witnessed and less likely to present with a shockable rhythm. Unadjusted analysis showed that females had a lower likelihood of survival than males (OR 0. 68 [0. 62-0. 74], I 2 = 97%). After adjustment, no significant difference was identified between male and female in survival at hospital discharge/30 days (OR 1.
Search strategy We systematically searched PubMed, Embase and Web of Science databases (inception to 23-April-2022) for papers reporting outcomes at the time of hospital discharge in adult (age 16 years or older) male and female after out-of-hospital cardiac arrest. The search was performed three times to ensure a full and up-to-date review of the literature. No language restriction was applied during the search, then only studies written in English were included. In brief, we used keywords as exact phrases and subject headings according to database syntaxes with the help of an information specialist. The full search strategy is described in Additional file 1.
Paper selection The titles and abstracts of all records were screened independently and in duplicate by two of the authors (IL, AN) using the Covidence software tool. The papers selected were downloaded in full and they were reviewed independently and in duplicate by the same two authors to verify fulfilment of inclusion criteria. The reference lists of relevant articles were searched for additional potentially pertinent articles (i. e. , snowballing method). All articles rated discrepantly by the two screening authors in the Covidence software were reviewed and discussed one by one by both authors and subsequently included only if both authors agreed on eligibility. In each stage disagreements were resolved by a third author (SE). Among papers reporting completely or partially overlapping data, we selected the paper displaying adjusted data and describing the greatest number of patients.
Since many of the included studies presented data from national databases, the risk of overlapping data was high. To avoid such overlap, we excluded any study that presented data from a country whose national database was already being used in another included study on the same period (Additional file 2: Table  S1 ). In this case, the study included was the one with adjusted data, and if more than one study from the same national database had adjusted data, the largest cohort was chosen. Although pre-planned, alternative prioritisation based on clinical sensibility due to poorer adjustment with more patients was never required. The list of papers with overlapping populations is presented in Additional file 2. We planned to contact the corresponding authors of the screened studies in case questions arose regarding eligibility or data presentation but no such issues arose.
Data extraction Two authors (IL, AN) extracted the data in duplicate using a standardized data extraction form. Discrepancies in the extracted data were adjudicated by a third author (SE). The data extracted included study characteristics (e. g. , source country, study type, single/multicentre), patient demographics (age, sex), medical background, treatments and outcomes. We also collected data on the type of adjusted analyses performed and the variables adjusted for. The final version of the database was validated by all the investigators involved in data collection (IL, SE, AN) and is available as Additional file 3.
There may be intrinsic differences in physiological responses to ischemia-reperfusion mechanisms which require elucidation. Finally post-resuscitation care may differ based on family request, professional concerns regarding potential risk/benefit ratio and more. Our study has several strengths. We prospectively registered the study protocol, performed analysis of adjusted data, assessed the RoB of the included studies and applied GRADE to assess the certainty of the evidence. However, any meta-analysis is only as good as the studies it includes. Most of the studies included in this review were judged at high RoB and neither randomization nor blinding are an option with regard to the study question at hand. Analysis of retrospective studies (regardless of real time data collection) carries an inherent risk of confounding due to study design.
05 [0. 98; 1. 12]-PI [0. 82; 1. 34] 80% < 0. 001 adjustment (as defined in the manuscript) Medium variables (3 studies) 0. 84 [0. 68; 1. 04]-PI [0. 12; 6. 07] 32% 0. 23 Weak variables (1 study) 0. 88 [0. 71; 1. 09] Not applicable Subgroup analysis by geographical location Asia (5 studies) 0. 94 [0. 84; 1. 06]-PI [0. 63; 1. 41] 85% < 0. 01 North America (7 studies) 0. 98 [0. 88; 1. 09]-PI [0. 71; 1. 34] 88% < 0. 01 Europe (7 studies) 1. 10 [0. 97; 1. 25]-PI [0. 79; 1. 53] 45% 0. 09 Oceania (2 studies) 1. 16 [0. 93; 1. 43] 0% 0. 54 Subgroup analysis by OHCA etiology Cardiac etiology (9 studies) 0. 99 [0. 86; 1. 14]-PI [0. 64; 1. 55] 77% < 0. 001 All etiologies (12 studies) 1. 02 [0. 95; 1. 11]-PI [0. 79; 1. 32] 90% < 0. 001 Subgroup analysis by type of cohort and National databases (8 studies) 0.
With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing.
05, 20 < Hue < 90). Finally, the GF of each plot was estimated as the ratio of the green pixels to the total number of pixels in the plot. The image analysis process was implemented in Python 3. 7 2  and library opencv (ver. 4. 1. 0) and gdal (ver. 3. 2. 2). For data in 2019, a similar procedure was used by Hiphen Inc. 3 The analysis protocol was the same as previous research  (Verger et al. , 2014; Madec et al. , 2017).
LAI amp is the maximum value of the LAI reached, r g is the maximum LAI growth rate, r s is the senescence rate, T g is the growing degree day when the LAI growth rate is maximum, and T s is the growing degree day when LAI becomes zero. The five parameters, LAI amp , r g , r s , T g , and T s , were estimated for each plot. However,  fitting the dynamics model separately for each plot was difficult because the GF in the drought treatment (D) was so small. Many of the GF growth data from the treatment D contained large noise, making it difficult to estimate the parameters, from data of each plot alone, especially the inflection point of the growth T g. Therefore, the estimation was conducted in two steps: (1) First parameter estimates. In this step, all of the parameters except LAI amp were assumed to be genotype dependent and treatment independent, that is, parameters of each plot were the same for treatments C and D if their genotypes were the same.
Estimation of Genotypic Values Genotypic values of the GF and LAI dynamic parameters were estimated for use in the GP. The following mixed model was fitted for each combination of a trait (GF or LAI dynamics parameter) and a treatment (C or D): y = µ1 + Lβ + Ws + e (4) where y is a vector of the phenotypic values; µ is the mean; β is a vector of block effects representing differences between replications; s is a vector of genotypic values that follows N(s | 0, σ s 2 I); σ s 2 is the genotypic variance; e is a vector of residuals that follows N(e | 0, σ e 2 I); σ e 2 is the residual variance; 1 is a vector in which all the elements are one; I is an identity matrix; and L and W are design matrices. The genotypic value (g) was calculated as follows: g = µ1 + s (5) The R package lme4 (ver. 1. 1-20) was used to solve Equation 4.