dataesr/scientific-paragraphs-categorization huggingface dataset:
Muscle Fiber Cross-Sectional Area The fiber cross-sectional area was determined by computerized image analysis on 10-µm thick sections cut using a cryotome MICROM HM 500 M at a temperature of -25 • C  [18]. The sections were stained with azorubine colorant to define the histological architecture of the muscle and to measure fiber proportion and diameter. Because the fibers did not have the same areas depending on animals and muscles, between 180 and 220 fibers from two different locations in the muscle were used to determine the mean fiber area using computerized image analysis. The surface area of each type of fiber and the mean fiber area were measured using the Visilog software program developed by Meunier et al. [18].
The proteins were fixed in a solution made with ethanol (30%) and acetic acid (5%) for 20 min at room temperature. The gels were incubated in a colored solution containing propanol 2 (25%), acetic acid (10%) and Coomassie blue R250 (2 g/L) for 20 min. Then, the proportion (%) of the three MyHC was determined by densitometric analysis using an ImageQuant Software (Amersham Biosciences/GE Healthcare, Uppsala, Sweden).
Statistical Analysis The purpose of our analysis was to create clusters of cull cows based on finishing practices and cull cow morphology, and thereby to compare the created clusters on their muscle composition and meat quality traits. Two multivariate analyses, according to finishing practices and cull cow morphology, were performed. First, two principal component analyses (PCA) were implemented using a ade4 package (acp. dudi procedure) in the R software on the following discriminating and active variables: (i) part of different forages, amount of concentrate, duration of the finishing period, and activity for the finishing clustering; (ii) birth weight, age at first calving, age at slaughter, calving number, estimated suckling value, and carcass weight for the cull cow type clustering. Data were automatically normalized in the analysis in order to give equal weight to each variable in the PCA analysis.
The other variables were not used because they were redundant or not discriminant. Afterwards, the item coordinates on the PCA dimensions were included in two hierarchical cluster analyses (HCA, ade4 package, cah procedure based on Ward's method, and R software). The program identifies the cluster, which has a minor variance within groups and the greater variance between groups. The HCA was done to identify different classes of finishing practices and cull cow types. The classes were reported in the database; each cow was associated with a class of finishing practices and cull cow type. Classes of finishing practices, of cull cow types and their interactions were compared on data used for PCA, muscle composition, and meat quality traits. Variance analysis and mean multiple comparisons with one factor were carried out using the general linear model (GLM) procedure from SAS.
When applied at a sublethal dosage, these drugs induce a sudden global relaxation of the chromosomal DNA, and the transcriptional response is then measured after a short time (usually 5-30 min), assuming that the latter then mostly reflects the direct effect of SC, rather than indirect effects influenced by the cell's response. Such data were obtained in many organisms (Table  2 ). Note that the reported number of affected genes is strongly variable; this variability might partly reflect actual differences between organisms, but is strongly affected by the experimental conditions and methods (relaxation level, transcriptomics technology, statistical analysis). Altogether, these data consistently demonstrate a very broad response to chromosomal relaxation, with a significant effect on more than one quarter of the genes.
To this end, we used captureerecapture data collected on a spatially structured population occupying 28 breeding patches. We took advantage of recent developments in multievent captureerecapture models to detect signs of context-dependent dispersal. The results revealed that the probability of successful reproduction and the number of newly metamorphosed individuals increased with both the size and the proportion of disturbance of a patch. In addition, our results showed that the factors affecting breeding success also influenced breeding dispersal probability. Large patch size negatively influenced emigration probability; in contrast, it positively influenced immigration probability. Equally, higher disturbance had a strong negative influence on emigration probability and slightly positively affected immigration probability.
Version postprint Comment citer ce document : Boualit, L. , Pichenot, J. , Besnard, A. , Helder, Joly, P. ,  Cayuela, H. (2019). Environmentally mediated reproductive success predicts breeding dispersal decisions in an early successional amphibian. Animal Behaviour, 149, 107-120. , DOI : 10. 1016/j. anbehav. 2019. 01. 008 length) of 36 mm in males and 37 mm in females; smaller individuals were excluded from the analysis. The sex was assessed on the basis of strong forearms with nuptial pads in males. We identified each individual by the specific pattern of black and yellow mottles on its belly, recorded by photographs. To minimize misidentification errors, multiple comparisons of individual patterns were performed using the pattern-matching software ExtractCompare  (Hiby & Lovell, 1990). ExtractCompare provides similarity scores between patterns scanned from photographs showing ventral patterns.
Two modelling steps were considered to highlight the successive processes of detection and breeding-state ascertainment. In the first matrix (Appendix 1), we introduced a set of intermediate observations: undetected (u), detection of eggs and larvae (l) and detection of newly metamorphosed individuals (m). This resulted in the consideration of two detection probabilities: one of eggs and/ or larvae (p1) and the other of newly metamorphosed individuals (p2). The second matrix (Appendix 1) specified the probability of successful reproduction conditional on these intermediate observations. This parameterization was implemented in the E-Surge program  (Choquet, Rouan, & Pradel, 2009). We ranked the models using the Akaike information criterion adjusted for a small sample size (AICc) and AICc weights.
2 ). The parameterization was implemented in the E-Surge program  (Choquet et al. , 2009) , which provides advanced diagnostics of numerical convergence and adds the benefit of refining biological parameter estimates by detecting redundant parameters. As the information about patch size was recorded over the entire 9 years of the survey, the models were run using the complete data set (2000e2008). Competing models were ranked using AICc and AICc weights. When the AICc weight of the best-supported model was less than 0. 9, we performed model averaging. The 95% confidence intervals, CIs, of model-averaged parameters were calculated using the parametric bootstrap method. Our hypotheses regarding recapture and stateestate transition probabilities were examined The state formulation includes four types of information: þ ¼ captured or o ¼ not captured at previous occasion, S ¼ stayed or M ¼ moved, s ¼ small patch or l ¼ large patch, þ ¼ captured or o ¼ not captured at current occasion.
Acknowledgments We warmly thank all the fieldworkers who assisted in data collection. This research project was funded by the  Lorraine Direction R egionale de l'Environnement, de l'Am enagement et du Logement (DREAL) , the  Agence de l'Eau Rhin-Meuse , the  Conseil R egional de Lorraine , the  Conseil R egional de Champagne-Ardenne , the  Conseil R egional de Picardie , the  Conseil G en eral de l'Aisne , the  Conseil G en eral d'Ard eche , the Conseil G en eral d'Is ere and the  Communaut e de Communes de l'Argonne Ardennaise  ( 2C2A ).
A coordinated effort, based on observing system simulation experiments (OSSEs), has been carried out by four European ocean forecasting centers for the first time, in order to provide insights on the present and future design of the in situ Atlantic Ocean observing system from a monitoring and forecasting perspective. This multi-system approach is based on assimilating synthetic data sets, obtained by sub-sampling in space and time using an eddy-resolving unconstrained simulation, named the Nature Run. To assess the ability of a given Atlantic Ocean observing system to constrain the ocean model state, a set of assimilating experiments were performed using four global eddy-permitting systems. For each set of experiments, different designs of the in situ observing system were assimilated, such as implementing a global drifter array equipped with a thermistor chain down to 150 m depth or extending a part of the global Argo array in the deep ocean.
, 2015). This timely project is part of a larger process recently initiated by the oceanographic community, to define a better-coordinated and sustainable in situ observing system in preparation for the OceanObs'19 conference, similar to what has been done specifically for tropical oceans (e. g. ,  Cravatte et al. , 2016). To support the on-going efforts undertaken by the oceanographic community, a coordinated initiative within the AtlantOS project has been conducted by European ocean forecasting centers, to provide quantitative information of potential impacts of evolved in situ networks on global ocean monitoring and forecasting systems. Several coordinated initiatives are currently handled in the framework of the Global Ocean Data Assimilation Experiment (GODAE) Ocean View  (Bell et al.
g. ,  Verrier et al. , 2017). However, the evolution of observing networks and operational forecasting and analysis systems requires an updated and refined understanding of observations' impact on numerical models. The present work is based on numerical experiments, called observing system simulation experiments (OSSEs), and is designed in order to assess the impact of a given observing system on a monitoring system. OSSEs consist first of sampling a "realistic" simulation at the location and time of each observation of a given observing system, and then assimilating these simulated observations into a data assimilation system. The observing systems, that have been considered in this study, result from exchanges and discussions with in situ network experts and mainly concern the global Argo floats and drifter arrays  (Roemmich and Gilson, 2009; Lumpkin et al.
, 2016). Monitoring and forecasting systems provide one of the few tools that allow the exploration of the integrated aspects of the Global Ocean Observing Systems (GOOS), including in situ platforms and satellites. OSSEs are therefore usually performed in order to support the evolution of an integrated global ocean observing system, but they can also help tuning data assimilation schemes in ocean reanalysis and monitoring systems, and prepare operational systems to ingest new observations (e. g. ,  Bonaduce et al. , 2018). The multi-system feature of this study is a crucial point ensuring the robustness of the results, which otherwise can strongly depend on the model and data assimilation scheme used  (Halliwell et al. , 2014). To our knowledge, this is the first time that a coordinated effort has been made using OSSEs, mainly because these numerical experiments require heavy and dedicated infrastructures.
Construction of the Synthetic Data Sets The Satellite Component The generation of the synthetic observations was based on subsampling the daily fields of the NR at the location and date of each observation. In order to deviate from the NR realization, noise was then added to these values -the observation error is discussed later. The SSH synthetic dataset was built from a constellation of the three satellites Jason-2, Sentinel-3a and Sentinel-3b. The Jason-2 trajectory (longitude, latitude, date) was extracted from CMEMS Sea Level TAC (Thematic Assembly Center) multimission along-track L3 altimeter products (as prepared by the DUACS system) for a 3-year period from 2009 to 2011 (10-day repeat cycle; around 13 orbits per day) due to a lack of more than 15 days in the Jason-2 dataset in 2008.
The Sentinel-3a/-3b orbits were theoretically determined (27-day repeat cycle: around 14 orbits per day; G. Dibarboure, personal communication). For the ARMOR3D system (CLS), SSH synthetic dataset consist of daily fields on a regular grid at 1/4 • horizontal resolution. For three groups, MOI, CMCC, and CLS, the SST synthetic dataset consisted of daily fields on a regular grid at 1/4 • horizontal resolution. The SST and sea ice concentration (SIC) synthetic datasets used for the UK Met Office OSSEs were produced by extracting NR values at the locations of the operational observing networks in 2016. This SST observing network consisted of L2 swat measurements from four infrared satellites (VIIRS onboard Suomi-NPP, and AVHRR onboard MetOp-B and NOAA-18/19), one microwave satellite (AMSR2), and in situ SST measurements (from drifting buoys, moored buoys and ships).
The in situ Component The synthetic in situ datasets consist of subsurface vertical profiles of temperature and salinity from mooring platforms, eXpendable BathyThermograph (XBT, only temperature), and Argo floats, which have been extracted from the CORA 4. 1 in situ database distributed by CMEMS In Situ TAC  (Cabanes et al. , 2013; Szekely et al. , 2016). Following discussions with mooring network experts, the mooring sampling during 2015 was chosen to represent the mooring sampling for the 3-year OSSE period (Bourles and Cravatte, personal communication) as one of the most representative periods of the Global Tropical Moored Buoy Array 1. The 2013-2015 drifters sampling was used for the OSSE (Poli, personal communication). The synthetic Argo designs were built considering the time, date and location of Argo profiles during the 2009-2011 period.
, 2017). Even if most state that the quality of the analyses was improved due to the assimilation of moorings, the quantification of the relative contribution of each component of the observing system (e. g. , fixed-points versus Lagrangian floats) remains an important challenge for operational centers. It should be mentioned that the high temporal frequency/sampling of moorings is not exploited by construction in current 3D data assimilation systems. With 4D ones, the impact could be potentially larger, as several profiles from moorings could be assimilated. In this study, the metrics used focus on the entire Atlantic and Gulf Stream regions, which might not be appropriate for a regional fixed-point array. Thus, this experiment is not presented in this study and further investigations and appropriately adapted metrics are therefore needed in order to assess the contribution of tropical moorings to the representation of the ocean state in the current forecasting and analysis systems.
Implementing a Deep Argo Array Due to its contribution to rising sea levels and the Earth's energy budget, the deep ocean is a crucial component of the ocean variability  (Purkey and Johnson, 2010). However, the performance of numerical models are limited by the lack of sufficient deep ocean observations. Such data are required for model initialization and assimilation, and are presently limited to sparsely repeated hydrographic sections embedded in international programs (e. g. , WOCE and GO-SHIP). The need for intense observations in the deep ocean, below 2,000 m, has been recognized by the scientific community. A deep Argo array is therefore expected to be deployed to carry out deep ocean sampling at a global scale  (Johnson et al. , 2015) , with the aim to achieve similar success than the core-Argo array, which has been sampling the upper-ocean (0-2,000 m) for more than 15 years  (Riser et al.
SUMMARY AND CONCLUSION Based on numerical experiments, further evolutions of the in situ component of the GOOS have been assessed by using four global eddy-permitting systems, including three analyses and forecasting systems and one statistical analysis system. The originality of this study lies in the assimilation of exactly the same synthetic in situ data sets, which are deduced by sub-sampling the Nature Run (1/12 • unconstrained system at Mercator Ocean International) in space and time for each observation of a given observing system design, and the use of this ensemble of simulations to get a 'confidence interval' on the impact of these evolutions. For each observing system evolution, at least two groups assessed the impacts of the integrated observing system on the monitoring and forecasting systems and have generally shown improvements in the representation of temperature and salinity fields.
, 2015) , e. g. , high sampling of data (moorings, radars, day-time satellite measurements) could be better exploited with 4D-VAR. Although the three DAS use the NEMO ocean model or similar atmospheric forcing, there are remarkable differences in the systems (e. g. , initialization, bulk formulas, and surface restoring) for which the ensemble spread tends to be reliable. It is, however, important to recognize that the higher the multisystem ensemble, using different systems and methods, the more robust the results are likely to be. Multi-model, multi-forcing ensembles could thus be envisaged in the future. Moreover, the experiments relied on the performance of the Nature Run, and any improvement of the free simulation, especially below the surface layer, should improve the results. The systems are tuned for a specific observation network and require time to adapt to a new one (e.
ACKNOWLEDGMENTS This study was conducted using Copernicus Marine Service Products. This paper used data collected and made freely available by programs that constitute the Global Ocean Observing System and the national programs that contribute to it (http://www. ioc-goos. org/),i. e. , the Argo data collected and made freely available by the  International Argo Project  and the national programs that contribute to it (http://argo. jcommops. org),the surface drifter data supplied by the  Global Drifter Program , the  TAO-PIRATA-RAMA (PMEL-NOAA ) and the  OceanSITES initiative  for various data sets. We thank two reviewers for their helpful comments.
Quick building damage assessment following disasters such as large earthquakes serves to establish a preliminary estimation of losses and casualties. These datasets are completed by employing several crowdsourcing initiatives, in which volunteers and collaborators map damaged buildings in a given area at a qualitative damage scale based on a post-earthquake aerial or satellite image. Automating this process is a temptation and a technical issue, but manual interpretation remains essential, with the identification of moderate and lateral damage being the key and limiting factor. Following the Haiti 2010 earthquake, many studies were completed by crossing multilayer data gathered from different sources (satellite, aerial, and field survey). These works created a building damage dataset that enabled the construction of different sets of empirical vulnerability functions.
In the present study, we proposed to review the datasets used for the damage assessment again, investigate how they can be managed for understanding urban damage patterns, and quantify the potentialities and limits of the sets. A high-resolution map of damage in Port-au-Prince was used to obtain a deducted map of intensity and was then compared to more detailed post-earthquake investigations such as the microzonation of the city  (Belvaux et al. 2018 ). These detailed post-earthquake investigations, in which array microtremor measurements are performed for characterization of the subsurface soil, contribute to a better understanding of local variations in intensity. Subsequently, a retro damage scenario was run, considering the different sets of vulnerability functions (using the RISK-UE methodology vulnerability indexes) fitted with empirical vulnerability functions.
Using the characterization of the exposure on a remote sensing basis, the results fit the heaviest damage well (building collapse), but they overestimated moderate damage states compared to the observations. However, is an aerial image based dataset sufficiently exhaustive for moderate damage, which is mostly visible from a lateral or internal point of view? Finally, we suggested some range of adjustments that can be applied to a vulnerability assessment originating from remote sensing data such that it can be used more accurately in the detection of urban damage, even for moderate damage degrees.
Even if techniques such as LIDAR have serious benefits (as for example to estimate debris volume), they found that optical images, satellite or aerial, remain the most reliable and independent evaluation. More recently,  (Cooner, Shao, and Campbell 2016)  presented a work testing three algorithms to automatically map post-earthquake damage based on machine learning methods with great results. However, they provided results in a binary form: damaged and undamaged, based on the most easily visible damage (total collapse). (Romaniello et al. 2017)  provides damage in terms of collapse ratio with automatic image processing using Haiti 2010 Earthquake dataset. Remote-sensing-derived data is also a prevention tool to estimate built-up areas and exposure to natural hazards such as earthquakes  (Ehrlich et al.
2018). Remote sensing can be used during all the phases of natural risk management (prevention, preparedness, crisis management, recovery, and reconstruction). The January 12, 2010, Port-au-Prince Earthquake in Haiti (M7-7. 1) killed more than 200,000 people  (Calais et al. 2010). It was felt in all Hispaniola Island (Haiti and Dominican Republic) and in the East part of Cuba. It was the first earthquake with an important work completed by several international organizations generating a building damage dataset in the Port-au-Prince area. The goal of this work was to estimate as soon as possible an order of magnitude of the damaged buildings and its economic impact for international donors. Therefore, in more recent earthquakes such as Amatrice in Italy, Ecuador, and Nepal in 2016, remote sensing has been used to aid damage assessment  (Karimzadeh and Mastuoka 2017; Piscini et al.
2017; Copernicus EMS 2016; Copernicus 2015). However, other potential uses of these datasets exist, such as the establishment of empirical vulnerability curves for Caribbean countries' building types. A very important work regarding the identification and cartography of damaged buildings was attempted for the 2010 Haiti Earthquake using several techniques for identification of the damaged areas and structures (remote sensing, aerial images, field inventory, etc. ). Based on these databases, some authors have proposed empirical fragility functions adapted to current Haitian buildings  (Hancilar, Taucer, and Corbane 2010; Hancilar, Taucer, and Corbane 2013; Molina et al. 2014). In another context, South American countries, (Villar-Vega and Silva 2017) run retro damage scenario for several past events and compared it with building damage datasets, in terms of collapsed buildings.
They used a set of fragility curves representative of these countries, but they did not adapt a new set of curves. Among the main limitations they observed was the variation in the portfolio when comparing models, based on recent census data, with past events 15 years old and the quality and heterogeneities (in terms of damage states) among the building damage datasets. The present work discusses these previous works in damage and vulnerability assessment completed after the 2010 earthquake and proposes a high-resolution intensity map based on observed damage in the Port-au-Prince area. This map was compared to the USGS ShakeMap (USGS 2017), which was calculated during the hours/days after the earthquake, and some authors consider it as the reference in terms of the intensity for this event.