dataesr/scientific-paragraphs-categorization huggingface dataset:
The direct mortality of adult females is a consequence of particular interest among long-lived mammals given the significant contribution to population growth often observed from adult female survival  [11, 12]. The extreme nature of major hurricanes may also lead to both significant mortality and suppression of fertility across stages, raising questions regarding the adaptive potential of populations and whether impacts to viability place a population under threat of local extinction. In this study, we analyse the long-term population dynamics of the free-ranging Cayo Santiago rhesus macaque (Macaca mulatta) population from 1973 to 2018 in order to examine the impacts on demography from major hurricanes during this time period. We examined years where the population experienced the direct effect of a major hurricane (category greater than or equal to 3) and use data on 4635 females structured into development and reproductive stages to parametrize matrix population models and robustly quantify variation in long-term population fitness following hurricane events.
We also assess the population-level effects of variation in vital rates during hurricane years in order to determine which life cycle transition contributed the most to changes in the long-term population fitness. The Cayo Santiago rhesus macaque population is ideal for addressing demographic effects of hurricanes, given the long-term data available and its location in the Caribbean region, which has experienced the direct effects of three major hurricanes since the 1950s and is expected to experience a higher frequency of major hurricanes in the following decades due to climate change  [1]. In particular, Cayo Santiago rhesus macaques bring the unique opportunity to robustly decompose the demographic effects of hurricanes into relevant life-history traits (stage-specific survival and reproduction) and allow us to know how long-lived animal populations, especially those with no long-term food limitation either due to broad diets, rapid food replenishment following a hurricane or whose principal food source is little affected by hurricanes, might respond to these extreme events.
Following this, survival of infants was set to 1, as only surviving infants were recruited into the population. Thus, each matrix element (q i,j ) corresponds to stage-specific vital rates; the probability of surviving and transitioning into another stage; the probability of surviving and staying in the same stage (stasis); and fertility ( f B , figure  2 ). Note that our model considers reproduction to occur before the stage transitions and thus, NB or FB present fertility = 0. For each matrix model, we determined the mean population growth rate (λ, the population long-term fitness), the stable stage distribution (w) and the reproductive value vector (v), by computing the dominant eigenvalue and corresponding right and left eigenvectors, respectively  [24]. To test for differences between individual transitions generated during non-hurricane and hurricane years, a bootstrap was performed using both the non-hurricane year dataset and the hurricane year dataset.
royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 7: 200173 Bootstrapping is a common resampling method to estimate measures of uncertainty for demographic parameters when using matrix models without parametric assumptions or balanced data  [24]. Major hurricanes are rare extreme events, they occurred only three times in our 45-year study and thus generated unbalanced demographic datasets. However, our analysis is based on data from all females in the population and thus, our population-level information and sample sizes are appropriate for this method. For this, we combined individual stage transitions from the 41 non-hurricane years  (1973-1989, 1990-1998, 1999-2017) , which included a total of 18 344 individual transitions. Similarly, we combined individual stage transitions from hurricane years  (1989-1990, 1998-1999, 2017-2018) , which included a total of 1816 individual transitions.
For each one of the datasets, individual transitions were randomly selected with replacement, until the original empirical sample size was obtained. Demographic transitions were calculated from this sample, allowing a transition matrix to be constructed and λ estimated. This process was repeated 1000 times and 95% CI were determined from 2. 5th and 97. 5th percentiles of the data  [24]. A two-sample Kolmogorov-Smirnov (K-S) test was performed to compare statistically both bootstrap λ distributions. Our demographic model incorporates stage-specific vital rates to estimate λ, a measure that synthesizes vital rates into population-level performance (i. e. population growth rate). Because λ presents different sensitivities to each vital rate, not all environmentally induced changes in vital rates affect λ equally.
For this, two mean matrix models were parametrized from individual transitions; one using the non-hurricane year dataset (G, reference matrix) and another for the hurricane year dataset (B, treatment matrix) and the matrix of contributions C was estimated. In this way, each entry in the matrix of contributions represents a contribution of the effect of hurricanes on its corresponding matrix entry (vital rate) to the overall hurricane effect on λ. Given density-dependent dynamics affecting fertility in Cayo Santiago females  [12] , we further evaluated the effects of treatment (i. e. hurricane) on mean age-specific fertility after controlling for population density (i. e. number of adult females) using generalized additive mixed models. Generalized additive mixed models are non-parametric extensions of generalized linear models that allow the evaluation of nonlinear relationships and thus, they are appropriate to model age-specific fertility in primates.
Considering all factor combinations, this resulted in a total of five competing models that were evaluated using Akaike's information criterion (AIC) for model selection  [28]. All analyses were carried out using R  [29] , v. 3. 5. 1 and packages Popbio  [30] , and gamm4  [31]. Data and codes for the analysis are deposited in Dryad  [32]. I Y J N B F B B q I q Y f B q J-NB q FB-NB q B-NB q NB q NB-FB q B-FB q FB q B q FB-B q J-FB q J-B q NB-B.
Acknowledgements. We thank members of  Cayo Santiago  and the  Caribbean Primate Research Center  who contributed to census data collection. The content of the publication is the sole responsibility of the authors and does not necessarily represent the official views of the  National Center for Research Resources ,  ORIP , or UPR. Cayo Santiago is supported by the  Office of Research Infrastructure Programs (ORIP)  of the  National Institute of Health , grant  2 P40 OD012217 , and the  University of Puerto Rico (UPR) Medical Sciences Campus. We also thank  Matt J. Kessler  and  John Berard  for their personal communications on the post-hurricane management of the colony following hurricanes Hugo and Georges, as well as  Diana L. Delgado  for providing the map of the study site, and  Michelle Lapierre Skrabut  for the Cayo Santiago aerial images.
Data and methods To quantify our understanding of surface melt over the northern GVIIS for the austral summers from 1979/1980 to 2019/2020, we analyse large-scale melt information from 25 km gridded passive microwave observations for both the AP and the northern GVIIS. For the northern GVIIS, these data are corroborated by smaller-scale (4. 45 km) active microwave observations available from 2007/2008 to 2019/2020. For austral summers from 2013/2014 to 2019/2020, we also calculate volumes of ponded meltwater https://doi. org/10. 5194/tc-15-909-2021 The Cryosphere, 15, 909-925, 2021 on the northern GVIIS from all available cloud-free optical images from the Landsat 8 (2013 to 2020) and Sentinel-2 (2017 to 2020) satellites. Both our microwave-derived melt and optical image-derived surface ponding results are evaluated alongside surface air temperature and wind data from the British Antarctic Survey (BAS) Fossil Bluff AWS (1979 to 2020) on the northwestern margin of the GVIIS (Fig.
The 1979 to 2020 brightness temperature time series was acquired by five successive sensors. The Scanning Multichannel Microwave Radiometer (SMMR), on the Nimbus 7 satellite launched in late October 1978, collected data at 18 GHz (while the sensor operated every other day, daily averaged brightness temperatures were used as input). Starting in 1987, the series of Special Sensor Microwave Imager (SSMI) sensors on the Department of Defense Meteorological Satellite Program (DMSP) platforms F8, F11, F13, and F17 collected data at 19 GHz. It is worth noting that there was a significant data gap between 3 December 1987 and 14 January 1988, and therefore we do not include any data from this melt season in our analysis. Although the melt data are provided with a spatial resolution of 25 km, the radiometers' 3 dB fields of view at 19 GHz are far larger (e.
Small-scale microwave scatterometer observations of melt For the northern GVIIS, we also derive smaller-scale melt information from an enhanced resolution C-band (5. 225 GHz) VV polarization radar backscatter image time series collected by EUMETSAT's Advanced SCATterometer (AS-CAT), aboard the tandem polar-orbiting satellites MetOp-A and MetOp-B. The 4. 45 km enhanced product is obtained by applying the Scatterometer Image Reconstruction (SIR) algorithm with filtering  (Lindsley and Long, 2016) , which is used to improve the spatial resolution of irregularly and oversampled data  (Early and Long, 2001). The effective spatial resolution was estimated at ∼ 12-15 km, three-fold finer than the effective resolution of the SMMR/SSMI-based product (∼ 50 km). For each day and for each grid cell, melt is assumed to be present when the ASCAT signal is lower than the winter mean signal minus 3 dB, as proposed by  Ashcraft and Long (2006)  using a melt model and QuikSCAT Kuband (13.
Landsat 8 and Sentinel-2 derived meltwater areas and volumes To calculate the time series of areal extents, depths, and therefore total volumes of surface meltwater lakes on the northern GVIIS for the seven austral summers from 2013/2014 to 2019/2020, we applied the threshold-based algorithm developed by  Moussavi et al. (2020)   Therefore, lake volumes derived from this sensor would not be easily comparable to Landsat 8 and Sentinel-2 and therefore would not necessarily extend the time series. Moussavi et al. 's (2020)  method, developed in parallel for Landsat 8 and Sentinel-2, combines separate threshold-based algorithms to detect (1) lakes, (2) rocks, and (3) clouds. Optimal thresholds for each band and band combination (e. g. Normalized Difference Water Index (NDWI), Normalized Difference Snow Index (NDSI), and others) were determined by creating a training dataset based on selected Landsat 8 and Sentinel-2 images, which represented spectral properties of several classes (e.
2. In total, for Landsat 8 we analysed mosaicked images for 191 dates from 6 December 2013 to 12 March 2020, and for Sentinel-2 we analysed mosaicked images for 14 dates from 3 January 2017 to 19 January 2020. Of those images, nine Landsat 8 and Sentinel-2 image mosaics had the same dates, and thus we merged those. First, we resampled the Sentinel-2 data (10 m resolution) to the resolution of Landsat (30 m). Second, we kept overlapping water-covered pixels in preference to dry pixels and kept the largest depths of the overlapping water-covered pixels. This resulted in a time series of 196 mosaicked images from 6 December 2013 to 12 March 2020 (Table  S1  in the Supplement). Errors and uncertainties associated with lake area and depth retrieval methods for each sensor are thoroughly discussed in  Moussavi et al.
3. 1 for more detail). Therefore, we cannot directly compare the microwave-derived melt data with the optical image-derived ponding data. However, together these data provide information on the time between melt onset and surface ponding over the northern GVIIS, and likewise the disappearance of surface ponding and melt termination at the end of the season. For the time period from 2013/2014 to 2019/2020, when we have three independent datasets, 2019/2020 was anomalous for the following reasons. Optical imagery indicates this season had the largest volumes of observed surface meltwater ponding (Fig. 5b ), microwave radiometer-and scatterometer- derived data show that it also had the most spatially extensive melt (i. e. 100 % of the AOI) for the greatest number of days (Fig. 5c ), as well as the highest number of cumulative melt days (Figs.
Conclusions We have used microwave radiometer data from 1979 to 2020 and microwave scatterometer data from 2007 to 2020 to show that the 2019/2020 austral melt season on the northern GVIIS was exceptional in terms of both cumulative melt days and areal extent compared to the previous 31 melt seasons since 1988/1989 and possibly since the beginning of the record in 1979/1980. We also used multi-spectral satellite imagery from 2013 to 2020 to show that the observed surface meltwater ponding on the northern GVIIS in 2019/2020 was also exceptional in areal extent and estimated volume since at least 2013/2014. Our analysis, based on the local weather data from the Fossil Bluff AWS, shows that sustained periods of warm (≥ 0 • C) temperatures from early in the season (late Novem-ber) likely contributed to the exceptional 2019/2020 melt event.
Acknowledgements. The authors thank  Steve Colwell  at the  British Antarctic Survey (BAS ) for help with the  BAS Fossil Bluff AWS  data acquisition. Doug MacAyeal ,  Ian Willis ,  Ted Scambos , and  Julie Miller  are all thanked for useful discussions, and  Julie Miller  is also thanked for producing the MODIS mosaic in the background of Fig. 1a. Financial support. Alison F. Banwell  received support from the  U. S. National Science Foundation (NSF)  under award no. 1841607  to the  University of Colorado Boulder  and from a  CIRES   Postdoctoral Visiting Fellowship. Rajashree Tri Datta  was funded by the  NASA ICESat-2 Project Science  office. Rebecca L. Dell was funded by a  Natural Environment Research Council (NERC)   Doctoral Training Partnership Studentship  (CASE with the  British Antarctic Survey , grant no.
Code and data availability. The code used to calculate areas and volumes of surface meltwater is available at https://github. com/mmoussavi/Lake_Detection_Satellite_Imagery/ (Moussavi, 2020a)  and described in detail in  Moussavi et al. (2020). A comprehensive dataset of Antarctic lakes from Landsat 8 imagery is available at https://doi. org/10. 15784/601401 (Moussavi,  2020b). The passive microwave melt product is available at http://pp. ige-grenoble. fr/pageperso/picardgh/melting/ (last access: 1 October 2020). The ASCAT enhanced resolution product is available at https://www. scp. byu. edu/data/Ascat/SIR/msfa/Ant. html (last access: 1 October 2020). Temperature data are available from the BAS Fossil Bluff AWS at 10 min intervals from 2006 to 2020 (https://legacy. bas. ac. uk/cgi-bin/metdb-form-2.
4] [5] [6] [7] [8] [9]  In France, a recent evaluation of the completeness of the ICU surveillance system for influenza cases, using capture-recapture methods, estimated the exhaustivity of the French national hospital discharge database (PMSI) to 73%. 10  Using the PMSI database in order to estimate the burden of severe influenza illness in hospital is consequently of relevance. The objective of this study was to assess the burden of hospitalizations with a diagnosis of influenza in France. In this aim, we described the characteristics and the severity of hospitalizations with a diagnosis of influenza by age-group and by season over five consecutive influenza seasons from 2012-2013 to 2016-2017.
| Data sources Data were extracted from the French national hospital discharge database (PMSI), which contains all records of hospitalizations in all public and private hospitals in France. Diagnoses are coded using the International Classification of Diseases, 10th revision (ICD-10). We extracted all hospital stays that occurred between 1 July 2012 to 30 June 2017 in metropolitan France with at least one ICD-10 code related to influenza (J09, J10, J11) as a principal, related or associated diagnosis. The information collected for each hospitalization was as follows: the unique patient identification number, the admission hospital identifier, the patient age, the diagnosis-related group (DRG), the week of admission at hospital, the length of stay, whether or not an admission to intensive care unit (ICU) occurred, and the issue at hospital discharge.
| Analysis The analysis covers five influenza seasons from 2012-2013 to 2016-2017. An influenza season was defined as the period between week 45 and week 15 of the following year. Hospitalizations outside the influenza period were not included in the analysis. In case of several stays in a same season, only one stay was kept in the final database preferably the stay with ICU admission otherwise the stay with a death record, or the stay with the most severe DRG, and the first stay if previous criteria were not discriminant. In the database, all hospitalizations are classified in the DRG system according to diagnoses, medical procedures, and demographic characteristics of the patients. 11 DRGs were used as a proxy of the reason of hospitalization. We defined five DRG classes: moderate influenza, severe influenza, respiratory distress, other respiratory diseases, and other DRGs.
| Definition of the virus dominance Virological data are not available in the hospital discharge database. In order to characterize the burden of influenza hospitalizations by dominant circulating virus each season, we used virological data from primary care settings. Data were obtained from the national reference laboratory for influenza viruses. A virus type or subtype was defined as a dominant virus if (a) it accounted for 70% or more of all isolates during the season or (b) if it accounted between 40% and 70% of all isolates and the second most common virus accounted for less than 30%. Two types or subtypes were considered as co-dominant if the main virus accounted from 40% to 70% of all isolates and the second most common accounted for 30% or more of all isolates. 12 e dominant virus was B/Yamagata in 2012-2013 (51%), B/ Victoria in 2015-2016 (70%), A(H3N2) in 2014-2015 (52%), and 2016-2017 (98%).
| D ISCUSS I ON This study described the characteristics and severity of hospitalizations recorded with an influenza ICD-10 code in the French national hospital database across five influenza seasons. Our results showed that influenza hospitalization rates by age-group each season were highly dependent on the circulating virus. Besides, the criteria of severity (ICU admission, lethality, and length of stay) were strongly dependent on the age of the influenza cases and were quite stable within each age-group across seasons.
| First attempt to estimate of the influenza burden on hospitalization in France Restricting influenza hospitalizations to hospitalizations with a diagnosis of influenza, we estimated that influenza epidemics caused 28. 5 hospitalizations per 100 000 inhabitants per season between 2012 and 2017. This rate was in the lower range of figures recently published by other countries with different case definitions and data sources. Based on the analysis of national hospital discharge database, the hospitalization rate was estimated at 49/100 000 in England between 1997 and 2009,  13  48/100 000 in Norway between 2008 and 2017,  9  and 33/100 000 in Canada between 2003 and 2009. 6 In Spain, based on data from active surveillance of confirmed severe influenza cases, the hospitalization rate was 7. 8/100 000 between 2010 and 2016.
| Main limitations Our study presents the usual limitations associated with the use of medico-administrative databases with coding rules based on economic and non-epidemiological criteria. Our definition of influenza hospitalization was thought to be very specific as we assumed that the attribution of an ICD-10 influenza code relies on a viral confirmation test as per guidelines recommendations. A previous study also showed that the exhaustivity of the hospital database for the surveillance of influenza cases admitted in ICU with a confirmed influenza diagnosis was about 73%. 10  While patients with influenza may present with various clinical pictures or clinical complications at a later stage, it may happen that no virological confirmation is performed  25  , which underestimate the burden of influenza hospitalizations.
| CON CLUS ION The analysis of hospitalizations with an influenza diagnosis using the national hospital database provides important information on the burden of influenza epidemics. Analyses of these data annually would be valuable in order to document the severity of influenza hospitalizations by age-group and according to the circulating influenza viruses. To get a more complete picture of the hospitalization burden related to influenza, further analyses need to be performed on acute respiratory infection hospitalizations without influenza diagnosis.
24 March 2020) and the Sense-City consortium (http://sense-city. ifsttar. fr/, last access: 24 March 2020) made available a dataset of optical disdrometer measurements stemming from a campaign that took place in September 2017 under the rainfall simulator of the Sense-City climatic chamber, which is located near Paris. Two OTT Parsivel 2 disdrometers were used. The size and velocity of drops falling through the sampling area of the devices of roughly a few tens of square centimetres are computed by disdrometers. This enables the estimation of the drop size distribution and the further study of rainfall microphysics or kinetic energy for example. Raw data -basically a matrix containing a number of drops according to classes of size and velocity, along with more aggregated ones such as rain rate and drop size distribution with filtering -are available.
(2012)  upgraded a portable rain simulator to investigate in detail the processes of runoff generation and erosion. In this paper we present disdrometer data collected during a measurement campaign aiming at testing a rainfall simulator installed in the climatic chamber of the Sense-City experiment. The campaign took place in September 2017. Before continuing, it should mentioned that the two disdrometers used here are already presented in  Gires et al. (2018a) , which deals with a previous outdoor campaign involving other instruments as well. Hence only the required basic elements on their functioning will be mentioned here, while the reader is referred to the previous paper for more details as well as a longer discussion on the potential uses of such data. To the Published by Copernicus Publications.
Brief reminder of the disdrometer functioning and available datasets As pointed out in the introduction, the measurement campaign uses devices whose functioning has already been described in detail in  Gires et al. (2018a). Hence only a short summary is given in this paper. The two devices are part of the TARANIS Observatory (exTreme and multi-scAle RAiNdrop parIS Observatory)  (Gires et al. , 2015 (Gires et al. , , 2018a, b) , b)  of the Fresnel Platform of École des Ponts ParisTech (https:// hmco. enpc. fr/portfolio-archive/fresnel-platform/ich, last access: 24 March 2020). The two optical disdrometers used here are OTT Parsivel 2 (see  Loffler-Mang and Joss, 2000; Battaglia et al. , 2010;  or the device documentation OTT, 2014), which are occlusion ones. It means that they are made of a transmitter, which generates a laser sheet and a receiver aligned with the transmitter with a sampling area of 54 cm 2 in between.