dataesr/scientific-paragraphs-categorization huggingface dataset:
A polarization diversity elastic backscatter lidar was deployed on the equatorial island of Palau in February and March 2016 in the framework of the EU StratoClim project. The system operated unattended in the Palau Atmosferic Observatory from 15 February to 25 March 2016 during the nighttime. Each lidar profile extends from the ground to 30 km height. Here, the dataset is presented and discussed in terms of the temperature structure of the upper troposphere-lower stratosphere (UTLS) obtained from co-located radiosoundings. The cold-point tropopause (CPT) was higher than 17 km. During the campaign, several highaltitude clouds were observed, peaking approximately 3 km below the CPT. Their occurrence was associated with cold anomalies in the upper troposphere (UT). Conversely, when warm UT anomalies occurred, the presence of cirrus was restricted to a 5 km thick layer centred 5 km below the CPT.
A cube polarizer is used to further divide the radiation at 532 nm in the components parallel and perpendicular to the polarization of the emitted light. Additional polarizers are placed in front of the detectors, which are miniature photomultiplier modules (Hamamatsu 6780-20) with low thermal noise (less than 10 counts/s at 25 • C). A characterization of crosstalk between the channels was performed following the method outlined in  Snels et al. (2009)  and found to be negligible. The photomultiplier signals are amplified with a bandwidth of 250 MHz, then recorded both in current and in photocounting mode by an acquisition card (Embedded Devices, APC-80250DSP). The two records can be merged during data processing. In current mode, the photomultiplier signal is filtered through a 15 MHz low pass to avoid aliasing and then digitized into an 8 bits waveform at adjustable sampling rates.
Data analysis Threshold values of 1. 2 for BR were used to identify the cloud base z bottom and top z top as limits for the calculation of the cloud integrated characteristics; clouds were identified as such in an altitude interval in which condition BR > 1. 2 was continuously met for at least 150 m. For them, the vertical extension of the cloud z and optical thickness τ were computed. The mid-cloud optical altitude z o , average temperature, average particle depolarization and average potential temperature were computed as weighted averages over the cloud vertical extension, with the weight given by the cloud particle backscatter coefficient β a. The mid-cloud geometrical altitude z g is defined as the average of the cloud top and base altitude. A back-trajectory analysis was conducted to investigate the thermal and convective history of the cloudy air masses.
The convective influence is individuated from the 3-hourly brightness temperature (BT) images at 11 µm from the climate quality Gridded Satellite dataset GRIDSAT-B1 (GRIDSAT78 BI)  (Knapp et al. , 2011). A convective source is therefore individuated when, in a specific geographical bin, a trajectory is travelling below (has a temperature warmer than) a convective cloud top level, selected from the satellite measurement with BT < 230 K, as similarly done in  Tzella and Legras (2011)  and  Tissier and Legras (2016). More details on the trajectory-convective cloud coupling methods can be found in  Bucci et al. (2020)  and  Legras and Bucci (2020). The age of the air masses is computed as the time elapsed between the time of release of the cluster, i. e. at the time of the lidar observation of the air mass, and the convective cloud crossing.
That coincided with a whole period of the Madden-Julian Oscillation (MJO), the eastwardmoving disturbance of clouds, rainfall, winds and pressure that traverses the tropics in 30 to 60 d on average. The upper panel of Fig. 1  reports the MJO phase diagram during the 50 d of the campaign. Recall that such diagrams represent the magnitude of the first two empirical orthogonal functions RMM1 and RMM2 of the combined fields of near-equatorially averaged 850 hPa zonal wind, 200 hPa zonal wind and satellite-observed outgoing longwave radiation (OLR) data  (Wheeler and Hendon, 2004 ). RMM1 and RMM2 data are from the Australian Bureau of Meteorology website (http://www. bom. gov. au/climate/mjo/, last access: 17 October 2019). Points representing sequential days are joined by a line, and their distance from the origin is re-lated to the strength of MJO cycle.
Acknowledgements. This study is funded by the  StratoClim  project by the  European Union Seventh Framework Programme  under grant agreement no. 603557. The authors gratefully acknowledge the support of  Justus Notholt ,  Institute of Environmental Physics, University of Bremen, Germany  and  Katrin Müller ,  Alfred Wegener Institute, Potsdam, Germany , who organized and guided the activities of the  Atmospheric Observatory in Palau ;  Sharon Patris ,  Coral Reef Research Foundation  and  Patrick Tellei ,  Palau Community College, Palau ;  Ingo Beninga  and  Wilfried Ruhe ,  Impres GmbH, Bremen, Germany , who very much assisted us remotely and on the field; and finally  Maurizio Viterbini ,  ISAC-CNR , now retired, but without whose invaluable technical support this work would not have been possible.
| Study population Of the 187 participants with presumed epilepsy, 144 had the epilepsy diagnosis confirmed by the neurologists. In addition, in 6 persons with EEG findings compatible with an epilepsy but who had not been considered as PWE based on the medical examination alone (no history of seizure/ no sufficient data to confirm the epilepsy diagnosis), the diagnosis was reconsidered, taking the EEG findings into consideration. Hence, our overall study population included 150 PWE. The average age was 26. 1 years, with a standard deviation (SD) 8. 6. Twenty-six participants (17. 3%) were adults, and 88 (58. 7%) were males. The complete description of the study population is presented in Table  1. Average age of onset of epilepsy was 11. 9 years (SD 6. 1). Nineteen (12. 7%) PWE had a history of nodding seizures, and 69 (46.
Statement of need Urban spatial properties are useful to study the urban climate: (i) basic parameters such as building fraction or building height are needed as input of parametric urban climate models such as the Town Energy Balance (TEB) 1  (Masson, 2000) , (ii) more sophisticated parameters are clearly correlated to urban climate observations 2 , and (iii) local climate classifications, useful for international comparisons, are mostly defined from urban spatial properties  (Stewart & Oke, 2012). Thus there is a need for tools dedicated to the calculation of urban spatial metrics. In previous research, scripts were developed to automatically calculate numerous indicators useful for urban climate applications  (Bocher et al. , 2018). These scripts have been organized, * corresponding author 1 https://github.
com/teb-model/teb 2 A few examples: (i) the lower the sky view factor, the higher solar radiation trapped by the urban canopy  (Bernabé et al. , 2015) , the higher the urban air temperature  (Lindberg, 2007) , the lower the wind speed  (Johansson et al. , 2016) ; (ii) the higher the density of projected building facade in a given direction, the lower the wind speed within the urban canopy  (Hanna & Britter, 2002). improved, and implemented within a Groovy library called GeoClimate. New urban properties and classification algorithms have been added. GeoClimate also simplifies access to geospatial data since it automatically downloads and organizes data from the worldwide OpenStreetMap database 3. One of the current major limitations for the climate community to use this data is its lack of building height information  (Masson et al.
State of the field and features comparison There is currently no software specifically designed for the calculation of geospatial indicators dedicated to urban climate. However, two software packages can currently be used to automatically perform some of the GeoClimate's features: • Urban Multi-Scale Environment Predictor  (Lindberg et al. , 2019) , available as a plugin in the free and open-source QGIS software, can be used for a variety of applications related to outdoor thermal comfort, urban energy consumption, climate change mitigation  (Lindberg et al. , 2018)  • Local Climate Zone Generator (LCZ Generator 4  ), available as an online tool, produces the LCZ classification of a given area  (Demuzere et al. , 2021). Table  1  shows the features covered by GeoClimate and for each feature the differences with UMEP and LCZ Generator.
The indicators in GeoClimate are calculated from vector GIS layers that represent the main topographic features: zone layer, building layer, road layer, railway layer, vegetation layer, water layer, and impervious layer (depending on the use of GeoClimate, only some of the inputs may be needed). To guarantee the use of the algorithms and their outputs, the GIS layers must follow a set of specifications. These specifications are defined for each layer. They include, notably, the name and the datatype of the columns, the values used by the attributes, and the dimension of the geometry. GeoClimate output data consists of both a set of indicators and classifications. GeoClimate uses the concept of a Workflow to chain a set of spatial analysis and statistical processes. The Workflow is organized in 3 steps (Figure  1 ).
Coding implementation GeoClimate algorithms are implemented as functions in Groovy scripts. GeoClimate is organized in 3 modules: GeoIndicators, OSM, and BDTopo_V2 (Figure  4 ). GeoIndicators is the main module. It contains all the algorithms to build the units of analysis, compute the corresponding indicators, and classify urban fabric by type. The SpatialUnits script creates the units of analysis (currently blocks and TSU). The BuildingIndicators, BlockIndicators, RoadIndicators, and RSUIndicators scripts calculate morphological and topographical indicators respectively at building, block, road, and RSU scales. The GenericIndicators script calculates indicators that can be applied to any scale (e. g. , the area of a unit -building, block, RSU -or the aggregation of indicators from one scale to an other -mean building height within a block or a RSU).
A minimal example GeoClimate can be executed directly in a command prompt or using the Groovy Console. In the following example, the GeoClimate OpenStreetMap chain is used through the command prompt to calculate TEB inputs, LCZ, and UTRF classification for Washington DC taken as an area of interest. After downloading the archive Geoclimate. jar and opening a command prompt in the same directory, the script can be called as: java -jar GeoClimate. jar -f configuration_file. json -w OSM The f option is used to set the path of the configuration file and the w option to specify the workflow type (OSM or BDTopo_V2). The configuration file sets the main parameters of the calculation, e. g. : { "description": "Processing OSM data for the Washington DC area", "input": { "osm": [ "Washington DC" ] }, "output": { "folder": "/tmp" }, "parameters": { "rsu_indicators": { "indicatorUse": [ "LCZ", "TEB", "UTRF" ], "svfSimplified": true, "estimateHeight": true }, "grid_indicators": { "x_size": 1000, "y_size": 1000, "indicators" :[ "BUILDING_FRACTION", "BUILDING_HEIGHT", "WATER_FRACTION", "VEGETATION_FRACTION", "ROAD_FRACTION", "IMPERVIOUS_FRACTION", "LCZ_FRACTION" ] } } } The configuration file is structured in four main parts.
The application of this approach on a synthetic case study shows that hydraulic and thermal HZ properties can be estimated from LOMOS-mini measurements, their identifiability depending on the Peclet number -summarizing the hydrological and thermal regime. Hydraulic conductivity values can be estimated with precision when greater than ∼ 10 -5 m • s -1 when other HZ properties are unknown, with decreasing uncertainty when other HZ properties are known prior to starting the LOMOS-mini measurement assimilation procedure. Water fluxes can be estimated in all regimes with varying accuracy, highest accuracy is reached for fluxes greater than ∼ 10 -6 m • s -1 , except under highly conductive exfiltration regimes. We apply the methodology on in situ datasets by deriving uncertainty-quantified HZ properties and water fluxes for 2 data points collected during field campaigns.
g. ,  Lapham, 1989; Constantz et al. , 2002; Rivière et al. , 2020). Various analytical and numerical methods based on the heat transfer equation have emerged (e. g. ,  Stallman, 1965; Hatch et al. , 2006; Rau et al. , 2014; Irvine et al. , 2020). Related software implementations are the VFLUX2 software using the steady state analytical solution  (Gordon et al. , 2012; Irvine et al. , 2015b) , the FAST program using the transient analytical solution  (Kurylyk and Irvine, 2016) , or the FLUX-BOT Matlab program using a numerical model for solving the advection-diffusion equation  (Munz and Schmidt, 2017). More recently, the Fiber-Optic Distributed Temperature Sensing (FO-DTS) technology was introduced for hydrological applications, opening new opportunities for inferring patterns of groundwater discharge into streams continuously along the streambed  (Selker et al.
4). The data assimilation chain and subsequent analyses were implemented with the R statistical software and the tidyverse collection of R packages for data science  (Wickham et al. , 2019; R Core Team, 2020). We implemented a suite of utility functions specific to our study in the form of an R package  (Wickham, 2015) , which can be found through a GitHub repository at https://github. com/kcucchi/HZinv.
Numerical Solution Temperature and pressure boundary conditions encountered in the field often didn't follow the hypotheses behind the analytical solution described in the previous section. Therefore, for field case study datasets, we solved the physical model described in section 2. 2. 1 using a numerical solution. The numerical model used is called Ginette  (Rivière et al. , 2014 (Rivière et al. , , 2019) )  and is available on the GitHub repository https://github. com/agnesriviere/ginette. It solves the flow and heat transport in saturated sediments using the finite volumes numerical scheme. We use a grid discretization of 1 cm and an adaptive time stepping ensuring convergence of numerical simulations. The hydraulic head difference measured by the LOMOS-mini device is used to define the hydraulic head boundary condition of the model, the top and bottom measured temperature time series are used as top and bottom temperature boundary conditions.
(A, B ) refer to the two LOMOS-mini measurement datasets presented in Figure  6 , corresponding to an infiltration and an exfiltration case study respectively. In each panel, values are normalized so that the same color scale can be applied, the x-axis is plotted on a log-scale. For grayed-out combinations of α e and κ e , numerical simulations have not converged within the allocated time (15 min) and no likelihood value can be computed. By comparing with the analytical study results (Figure  3 ), point (A) seems to undergo a conductive regime where high likelihoods are restricted within a narrow interval of κ e and high likelihood are bounded by a maximum value for α e , while point (B) seems to undergo a transition regime where both κ e and α e can be well-characterized from LOMOS-mini measurements.
(2020) , leading to uncertainty-quantified estimates of in situ water and energy fluxes under transient conditions with a temporal resolution of a few minutes, as well as associated uncertainty-quantified HZ properties. While it is well-established today that high-frequency datasets are required to fully capture the biogeochemical functioning of river and streams systems, the hyporheic datasets remain coarse as regards to time sampling  (Aubert et al. , 2015; Vilmin et al. , 2016; Floury et al. , 2017; Escoffier et al. , 2018; Wang et al. , 2019). Rapid hydrological events such as storms can be key drivers of increased exchange between stream water and groundwater in the HZ, thereby fueling HZ nitrate reduction processes and modifying the large-scale biogeochemical function of the HZ (e. g.
The methodology introduced in this study can not only serve as an in situ validation system for these innovative measurements, but also complement experimental set-ups for high-frequency discharge flux estimation, in particular at hot spot locations  (Zhao et al. , 2021). FO-DTS systems have recently been coupled with vertically distributed temperature sensors for quantifying water exchanges along the FO  (Shanafield et al. , 2018; Le Lay et al. , 2019). Since our methodology allows to lift the classical assumption of a daily hydraulic steady state, coupling FO-DTS with LOMOS-mini monitoring systems would open the door to the interpretation of FO-DTS datasets collected outside of low-flow steady-state events. This would provide unique datasets for characterizing not only HZ properties, but also HZ water and energy fluxes both continuously along the stream and during transient high-flow events, such as floods or flow reversal following a rainfall event.
ACKNOWLEDGMENTS The authors are grateful to  Aurélien Baudin ,  Asma Berrhouma ,  Noëlia Carrillo ,  Nelly Martineau , and  Edmée Cuisinier  for their contribution to field work. The authors acknowledge the feedback of  Prof. Sally Thompson  which helped improve the quality of the manuscript. This research used resources of the  National Energy Research Scientific Computing Center (NERSC) , a  U. S. Department of Energy Office of Science User Facility  located at  Lawrence Berkeley National Laboratory , operated under Contract No. DE-AC02-05CH11231.
The intensity of herbicide applications was assessed using two quantitative indicators  [50] : (i) the amount of active substances, which is the sum of the amount of active substances applied, and (ii) the Treatment Frequency Intensity (TFI), which is a measure of the intensity of herbicide application related to the recommended application. The intensity of mechanical weed control was estimated using the average depth of the soil operations. Nitrogen inputs, herbicide applications and mechanical weeding were considered from harvest of the previous crop to the weed sampling date. Data are summarized in the electronic supplementary material (Table  S4 ). Landscape information was obtained from the land-use database of the LTSER Zone Atelier Plaine & Val de Sèvre  [48]. We considered four landscape variables previously shown to affect weed species assemblages, i.
We included temperature (sum of growing degree days, • C) and rainfall (mean precipitation, mm) during the growing period of weeds, rather than throughout the year because these two variables are directly related to plant growth  [52, 53]. All these variables were included in linear models (LMs) for weed species richness and abundance in the two field compartments (i. e. , four LM models were built). We used a variable selection procedure comparing models based on minimizing the Bayesian Information Criterion (BIC  [54] ) using the dredge function of MuMIn package  [55]  in R software version 4. 0. 3 [56]. All explanatory variables involved in at least one of the models with a BIC difference lower than two, from the model with the lowest BIC, were kept for the second step. In a second step, we examined how the landscape context affects the importance of competition and disturbances on both weed species richness and abundance in field cores and field margins.
We built LMs (one for each weed metric in each field compartment) that included the variables retained in the model selection procedure performed in the first step and landscape variables, i. e. , hedgerow density and the amount of organic farming, seminatural habitats and oilseed rape fields. We also included the interactions between each landscape variable and the retained variables. The effect and spatial extent of each landscape variable were simultaneously estimated using the Bsiland function of the R package Siland  [32]. We used a type III analysis of variance ('car' R package, third version  [57] ).
Acknowledgments: We would like to express our thanks to  Stanislas de Closets ,  Thierry Fanjas-Mercere ,  Brice Livoir ,  Jonathan Millaud  and  Solenne Rousselet  for conducting the weed surveys and to  Ambroise Lahu  for checking the agricultural data and computing the herbicide and fertilizer indices and the number of soil operations. We also sincerely thank the farmers of the  LTSER Zone Atelier Plaine & Val de Sèvre  for their involvement in our research programs.
Funding: This project was supported by the  French Ministry of Ecology  project ( 2017-2020  "Pollinisateurs"), the  ANR IMAgHO  ( ANR-18-CE32-002 ) and the  INRAE MP   SumCrop  project ( 2020-2021 "INITIATE" ). This project also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement SHOWCASE No 862480. AB is supported by  CNRS  and the  Nouvelle Aquitaine Region  project ( 2018-2022  " Multistress "). SG and OM are funded by  INRAE.
INTRODUCTION Trait-based approaches in ecology and conservation are increasingly applied as the fields shift toward prioritizing an understanding of ecosystem functioning and maintaining ecosystem services  (Madin et al. , 2016a; Kissling et al. , 2018; Barnett et al. , 2019). Such an approach allows for mechanistic insight into how species interact with, react to, and shape their habitats  (Violle and Jiang, 2009; McLean et al. , 2018). The application of trait-based functional ecology to coral reef ecosystems is still in its nascent stages  (Bellwood et al. , 2019). The launch of the Coral Trait Database in 2016  (Madin et al. , 2016b)  was a milestone to consolidate the use of coral traits, much like the publication of the first terrestrial plant functional trait handbook  (Pérez-Harguindeguy et al.
MATERIALS AND METHODS We examined response traits in relation to three disturbances and effect traits in relation to four processes common on coral reefs. The disturbances were pollution, fishing, and climate change  (Hughes et al. , 2010 (Hughes et al. , , 2017)). The processes were herbivory, bioerosion, predation, and nutrient recycling  (Villéger et al. , 2017; Brandl et al. , 2019). These limits to inclusion of disturbances and processes were applied to structure the review through the response-and-effect framework. A comprehensive search of the literature was conducted using the Web of Science database ). An initial search on September 26, 2018 used the terms: Topic Sentence (TS) = ((fish * AND coral AND reef * ) AND trait * AND ("functional diversity" OR "functional evenness" OR "functional richness" OR "functional dispersion" OR "functional divergence" OR "functional redundancy" OR "functional group * " OR "functional complimentary")) OR TS = (("coral reef " OR "coral reefs") AND (fish OR fishes) AND (trait OR "life history") AND (function OR functions OR functional OR process OR processes OR disturbance OR disturbances)).
This search yielded 380 titles. Further search terms were used to find papers specifically related to relevant ecosystem processes and/or disturbances. For example: TS = ((fish * AND coral AND reef * ) AND trait * AND "climate change"). A total of 227 papers were found with these follow up search terms. Furthermore, the reference lists of four review papers that appeared in the searches were "snowballed". A total of 17 new references were added to the database using this method. Paper inclusion criteria were that (1) one or more of the processes or disturbances were being investigated in relation to (2) one or more traits of coral reef fish. Papers were excluded if (1) they were looking specifically at larval fish traits (e. g. , pelagic larval duration), (2) the process or disturbance was not one that is being reviewed for this paper, (3) the paper was not accessible or not in English, and/or (4) the paper was a review article (although references were checked and snowballed).