dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODS The empirical material discussed in this paper has been collected in the framework of a research project funded by the European Commission called STAR-FLOOD (http://www. starflood. eu/). Within this project, policy analysts and legal scholars in Belgium, England, France, the Netherlands, Poland, and Sweden conducted empirical analyses and evaluations of flood risk governance in their country, both at the national level and at the level of three case studies focusing on specific urban areas that were used to illustrate and further explore developments at the national level. As part of this research, an explorative assessment was made of whether or not, and to what extent, the three capacities discussed in the previous section were present. Data collection methods applied in all countries are desk research (analysis of policy documents, legal texts, case law, literature); semistructured interviews (70 in Belgium, 61 in England, 64 in France, 45 in the Netherlands, 54 in Poland, and 19 in Sweden), and at least one workshop with stakeholders in each country  (Alexander et al.
2016b , Ek et al. 2016 , Kaufmann et al. 2016 , Larrue et al. 2016 , Matczak et al. 2016 , Mees et al. 2016). The topics about which data were collected included factual information about the presence and relative importance of flood risk management strategies, their institutional embedding in flood risk governance arrangements, and actors' information and opinions regarding various aspects of flood risk governance in their country. Next to this, there were many occasions on which the comparison of strategies, arrangements, and resilience capacities was discussed with all six country teams. Because of this breadth of the paper's empirical knowledge base and our ambition to provide an overview at a relatively high level of abstraction, it is necessary to present the findings in a condensed way.
Acknowledgments: This paper has been written in the framework of the European Union's  Seventh Programme for Research, Technological Development and Demonstration  within the  STAR-FLOOD  project. This research has received funding from the  European Commission  under grant agreement no. 308364. We would also like to thank all researchers within the project and all who participated otherwise for contributing to the material upon which this paper is based. We furthermore thank  Tina Newstead  for her language corrections.
The operationally defined method to quantify atmospheric mercury fractions is similar to well established methods used for other "sticky gases" ammonia and nitric acid. Atmospheric CO and ozone concentrations were continuously measured using the TEI 48CTL gas filter correlation analyzers and 49C Ozone analyzer (Thermo Environmental Instruments Inc. USA), respectively. Detailed information regarding the principle of the instruments, calibrations, and measurement uncertainties can be found in a previous study  (Gheusi et al. , 2011). The standard uncertainties associated with CO and ozone datasets (15-min averaged data) were reported to be 6. 6 and 1. 2 ppbv, respectively  (Gheusi et al. , 2011). Atmospheric aerosol number concentration in PM 10 particles (i. e. of diameter<10 μm) was measured at PDM Observatory using a condensation particle counter (CPC), Model 3010 by TSI Inc.
Acknowledgments: This work was supported by research grant  ERC-2010-StG_20091028  from the  European Research Council  and the  National Science Foundation of China  ( 41473025 ,  41273145 ). We acknowledge technical support from the  UMS 831 Pic du Midi  observatory team. Beyond mercury speciation, other observational data were provided by the PAES atmospheric monitoring service supported by  CNRS-INSU.
Acknowledgments The initial version of this article was written during a summer visit of the  St. Giles laboratory of Human Genetics of Infectious diseases, Rockefeller University, New York , at the invitation of  Prof. Jean-Laurent Casanova. We wish to thank him and his collaborators for providing us with ideal working conditions. We also thank  Thomas Pradeu ,  Gladys Kostyrka , and  John Dupré  for their useful comments on earlier versions of this article. The  IGS laboratory  is partially supported by the  Centre National de la Recherche Scientifique  and  Aix-Marseille University.
Material and methods We sourced published population-level DNA sequence data from Genbank (http://www. ncbi. nlm. nih. gov/genbank/) for broadly distributed Indo-Pacific reef-associated fish and invertebrates with a partial distribution in the Coral Triangle region. We used data for species that had sequences from three or more locations, where eight or more individuals had been sampled from each, and where at least one of the locations sampled was within 2500 km of our approximated centre of the Coral Triangle (approximated as 1835 0 S, 135820 0 E, following maps in Green & Mous  [3] ; figure  1 ). In total, this yielded data from 45 species, 314 populations in total and an average of 6. 97 (range 3 to 23) populations per species (electronic supplementary material, table  S1 ). We aligned data for each species separately using Clus-talW in DAMBE v.
4 ha of surveyed vegetation, 14,155 trees were measured and 4480 herbarium vouchers collected and identified. Tree dry biomass was computed using a pantropical allometric model that includes tree diameter, tree height and wood specific gravity as explanatory variables  (Chave et al. , 2014) , and carbon content was derived using the standard conversion factor of 0. 47  (McGroddy et al. , 2004). Wood specific gravity, a constitutive factor of the aforementioned equation, was obtained from the Global Wood Density Database  (Zanne et al. , 2009). When species were not found in the database, the genus-level average wood density was used instead. Any unresolved cases (ca. 1% of all specimens) were assigned the value of the mean wood density for tropical Southeast Asia (0. 57 g cm -3 ; see  Chave et al.
, 2012). As we only had a restricted number of soil samples in peatlands and swamp areas (n = 8), we also reviewed the literature in search of data of (1) soil carbon density or (2) carbon content and bulk density from sampling points geolocalized in the study area. We were only able to retrieve information about two sampling points from one study  (Anshari et al. , 2010). We used Fisher's α as an indicator to characterize TAD. Fisher's α, a parametric index, is relatively independent of sample size and insensitive to the presence of rare species  (Colwell, 2009; Parmentier et al. , 2011). For details about sampling strategy, indices computation, and plot structural and compositional features, see Appendix A. Field estimates of ACD, SCD and tree diversity were compared with existing maps  (Saatchi et al.
Potential explanatory variables to be tested for model building We chose potential explanatory variables that are commonly used in the literature for this type of modeling and for which spatial data were available over the study area (Table  1 ). We worked at a 250 m grid cell resolution, which is the spatial resolution of MODIS MOD13Q1 (vegetation indices) and MOD44B (vegetation continuous fields) products. These products have been used widely for spatialization of various ground-measured vegetation-related response variables (e. g. see  Nagler et al. , 2007; Saatchi et al. , 2008). All geographical information was projected using a Universal Transverse Mercator projection (zone 49 N, WSG 84 datum). The MODIS MOD13Q1-derived vegetation indices (EVI and NDVI) have a temporal resolution of 16 days (23 images/year).
We gathered images from 2011, 2012 and 2013 to cover the entire fieldwork period. For both EVI and NDVI, we computed a maximum, mean and standard deviation for each grid cell over the 3-year period (i. e. 3 × 23 = 69 images). The MODIS MOD44B-derived vegetation continuous fields (percent tree cover, percent non-tree vegetation and percent non vegetation) have a temporal resolution of one year. We gathered images from 2011 to 2013 and computed the average value for each of the three vegetation continuous fields. Digital elevation data from the NASA Shuttle Radar Topographic Mission were obtained at a 90 m resolution  (Jarvis et al. , 2008)  and further resampled to 250 m. Slope was computed from original data and subsequently resampled to match the designated study resolution. We also used WorldClim dataset, which provides interpolated estimates of various bioclimatic variables for the 1950-2000 period with a 30 arc-second resolution (∼1 km resolution at the equator; see  Hijmans et al.
, 2005) , and from which we extracted mean annual temperature, temperature seasonality, temperature range, mean annual precipitation, precipitation seasonality and precipitation range (all resampled to match our 250 m resolution). Using ArcGis 10, we computed euclidean distances from three potential disturbance sources: roads (including logging roads), rivers and villages. A raster of minimum distance to potential disturbance source was created by selecting the minimum value between the three potential disturbance sources for each grid cell. Land allocation (i. e. the designated use of an area, e. g. production forest or national park) and soil data were also considered potential explanatory variables (obtained from the Ministry of Forestry and the Indonesian Soil Research Institute, respectively; see Table  1  for respective classes).
For each response variable, we grew a random forest of 1000 trees with the 20 potential explanatory variables. We then selected variables based on variable minimal depth and importance value (information available as model outputs; see  Ishwaran and Kogalur, 2015). In addition, we eliminated the variables that showed unexplainable partial dependence behavior or whose distribution in observations was not representative of the entire study area. Details about random forest regression algorithm and explanatory variable selection are given in Appendix C. Once explanatory variables were selected, new 1000-tree random forests were grown for each response variable and their performances in predicting dataset response values recorded. Random forests were finally used to predict response values over the whole study area.
Relationships between response variables, spatial congruence of their hotspots and potential threat analysis We used spatial regression models to study relationships between pairs of response variables based on our predictions. Soil type (mineral vs. peat) was explicitly incorporated in our models to test its influence on relationships between pairs of response variables. Analyses were performed at three different spatial resolutions (250 m, 1 km and 10 km) on a random subset of values (n = 250, i. e. ca. total number of non-empty grid cells at the coarsest resolution). At 1 km and 10 km spatial resolution, values were drawn among aggregated grid cells for which at least half initial grid cells had values. Analyses were done on original, square root-or log-transformed values, whichever format led to data distribution closest to normality.
e. 1%, 4% or 9% depending on threshold). We worked at a 10 km spatial resolution (i. e. the coarsest spatial resolution of existing maps) to jointly assess the spatial congruence of hotspots of response variables from our predictions and those of existing maps. Only results using the 10% threshold are presented here (see Appendix F for results using the 20% and 30% thresholds). To assess potential threats to hotspots of response variables over the study area, we classified hotspot grid cells according to the land allocation under which they are situated, and recorded whether the corresponding areas were included in logging, mining or plantation concessions (concession data obtained from the Ministry of Forestry). We only performed this potential threat analysis on our predictions and therefore worked at the original (i.
We also evidenced a saturation effect from both existing maps (i. e. none to few of their predicted values exceeded a threshold of about 175 Mg ha -1 ; Fig. 3(a)-(b) ). There was a significant positive relationship between our predictions of SCD and the top 30 cm soil carbon values in the Harmonized World Soil Database despite the limited number of unique values of the latter (Fig. 3(c) ). Comparisons of our predictions of TAD with three existing maps showed mixed results, with relationships being either negative or positive depending on which map was used (see Fig. 3(d)-(f) ).
Limitations of our predictions and those from existing maps, and perspectives on spatial congruence analysis Despite the fact that we used the best available data, the main limitations of our predictions arise from: (1) the still restricted size of our measurement dataset, and (2) the reliability of explanatory variable data. We tried to sample at least 2 ha per main vegetation type while an inventory of 4-6 ha would for instance be recommended to assess biomass of the lowland and hill dipterocarp forests of Indonesia with an error margin not higher than 6%-8%  (Laumonier et al. , 2010). More vegetation and soil sampling will be required to challenge our predictions and gain a better knowledge of carbon and tree species distribution over the study area. Our predictions also depend on the choice and reliability of explanatory variable data.
, 2013). While significant negative relationships were found between our predictions of TAD and those from  Slik et al. (2009)  and  Raes et al. (2009) , more recent predictions from  Raes et al. (2013)  are better aligned with ours. Species distribution models used in  Raes et al. (2013)  were obtained from a larger database of collection records and did not suffer from partial distribution modeling  (Raes, 2012) , as in earlier predictions. While hotspot-based approaches are widely used to study spatial congruence between ecosystem services (e. g. see  Egoh et al. , 2008; Eigenbrod et al. , 2010; Locatelli et al. , 2014) , such approaches might face limitations regarding spatial conservation prioritization. Conversely, the use of alternative tools such as Marxan  (Ball et al. , 2009)  or Zonation  (Moilanen et al.
Acknowledgments We are deeply grateful to villagers from  Keluin ,  Nanga Dua, Belatong ,  Banua Tengah  and  Nanga Hovat  for their hospitality and help in data collection. We warmly thank  Bapak Engkamat  for his precious help and support during fieldwork. We are thankful to  Bapak Bundani  from  Polisi Kehutanan  who joined the team and helped during surveys conducted in the  Betung Kerihun National Park. We are much indebted to  Bapak Ismail Rachman  from the  Herbarium Bogoriense  for voucher identification. We are also sincerely grateful to  Niels Raes  and  Ferry Slik  for sharing data from previous work. This research received financial support from  CIRAD ,  CoLUPSIA  ( EU  financed project DCI),  AusAid (Agreement 63650 with Center for International Forestry Research) ,  ABIES  ( Ecole Doctorale Agriculture, Alimentation, Biologie, Environnements et Santé ) and  CRP-FTA (Consortium Research Program on Forests, Trees, and Agroforestry).
2013). The domain of the numerical simulation is 1600 m × 1700 m as shown in Fig. 1(a). Due to the limitation of computation memory, we downsampled the DEM to a 5 m grid. We prepared two topographic data sets from the DEM; the sliding surface and the mass thickness on the surface. The sliding surface was constructed by taking the lower values of the DEMs before and after the landslide. The thickness of the sliding mass was computed by subtracting the DEM for the sliding surface from the DEM before the landslide. We used three-component forces obtained from a seismic waveform inversion in Fig. 2 (a)  (Yamada et al. 2013). In  Yamada et al. (2013) , the normalized residual of the observed and simulated waveforms is 0. 08, which suggests the average error of the amplitude is about 8 per cent.
A C K N O W L E D G E M E N T S We acknowledge the National Research Institute for Earth Science and Disaster Prevention for the use of F-net data. Data are available at http://www. fnet. bosai. go. jp/top. php. High-resolution DEM data, which have been used to calculate landslide volumes, were provided by the Nara Prefectural Government and the Kinki Regional Development Bureau of the Ministry of Land, Infrastructure and Transport. This research is funded by the John Mung Program (Kyoto University Young Scholars Overseas Visit Program) in 2014, the ANR contract ANR-11-BS01-0016 LANDQUAKES, CNCSUEFISCDI project PN-II-ID-PCE-2011-3-0045, the USPC PAGES project and the ERC contract ERC-CG-2013-PE10-617472 SLIDEQUAKES. We appreciate for reviewers providing very useful comments to improve our manuscript.
Acknowledgements We gratefully thank  Jon Fjeldså ,  Mike Crisp ,  Dan Faith  and  Roseli Pellens  for their comments on the manuscript, which greatly helped clarifying the ideas presented here. We also took benefi t of reading other chapters of the book that provided rich reviews about various aspects of phylogenetic diversity and that allowed more maturation than just cross-referencing. Finally, we are also grateful to many people who commented on previous versions of our thoughts about relicts in other contexts.
Statistical Analysis Eight females in total were studied. For that, two females were studied for each period (pregnancy, quiescence, after parturition or end of the second year of the cycle, and preparation to breeding); two slides were observed for each organ of female; the number of apoptotic or proliferating positive cells per thirty cells was determined for each cell type. The Nis element BR software (Nikon, Champigny sur Marne, France) was used. To estimate the significance of differences between obtained means, LSD Fisher's test was conducted. The probability Pr for differences between the means to be insignificant was computed. Differences were considered as significant for Pr < 0. 05.
Acknowledgments: The authors thank  Lyon Catholic University (UCLy)  for providing funds for the research project and for covering the costs to publish in open access. Author Contributions:  Michel Raquet  and  Jean-Marie Exbrayat  conceived and designed the experiments; Michel Raquet performed the experiments; and  Michel Raquet ,  Claire Brun  and  Jean-Marie Exbrayat  analyzed the data and participated in writing the paper.
We obtain mixing ratio retrievals which agree above the coldpoint tropopause to within 1. 9 and 0. 5 % for the first and second flights, respectively. This level of agreement for balloonborne measured stratospheric water mixing ratio constitutes one of the best agreement reported in the literature. Because both instruments show similar profiles within their combined uncertainties, we conclude that the Pico-SDLA H 2 O and FLASH-B data sets are mutually consistent.
Both sets of line parameters are obtained from HITRAN 2012 database  (Rothman et al. , 2013). In HI-TRAN, the line intensities for these two lines are based on the work by R. A. Toth at JPL (Jet Propulsion Laboratory, NASA) with a relative uncertainty of 2 % (see "Linelist of water vapor parameters from 500 to 8000 cm -1 " at http: //mark4sun. jpl. nasa. gov/h2o. html). The water vapor transition is determined prior to the launch, thus allowing for automatic selection during in-flight measurements. The mixing ratio is extracted from the measured spectra using a nonlinear least squares fitting algorithm applied to the measured line shape. We use the Beer-Lambert law to model the spectrum and use a Voigt profile (VP) to describe the molecular line shape. In the case of the VP, the self-and air-broadening effects are taken into account.
Temperature and pressure measurement comparison on 18 January 2013 On 18 January, Pico-SDLA was launched at 22:11 UTC under a 1500 m 3 balloon. The time is recorded in UTC using a GPS-disciplined clock located on board the Pico-SDLA. One measurement is made every 300 to 500 ms depending on the SNR of the measurements and on the vertical speed of the payload during the flight. The measurements start as soon as they are requested by the operator, independently of launch time. The RS-92 radiosonde, attached to the same balloon, detects the launch time and records it as t = 0. Thereafter, it takes one measurement every 10 s. The data were synchronized by applying a small temporal offset to the time stamps. This offset was determined from the cross-correlation of the temperature profiles from both sensors and corresponded to the maximum of the crosscorrelation.
6 km), above which the atmosphere is governed mainly by stratospheric processes (green dash line in Fig. 4 ). The temperature profiles are also shown in orange and brown lines. The CPT is much colder in this case than for the 10 February flight (-79 • C in average instead of -74. 6 • C). The RS-92, integrated into FLASH, measures the geopotential altitude whereas the GPS on board Pico-SDLA measures the geometric altitude, inducing a shift of 378 m in altitude. To correct for this difference, we used the altitude measurements from the COBALD sonde, which are obtained from a GPS. Thus, we were able to reconstruct the FLASH altitude scale by interpolating the COBALD data with respect to the time into flight. The same operation has been applied for the 10-11 February 2013 flight for which no shift remains.
Acknowledgements. This work and the  TRO-Pico  project were supported by  Agence Nationale de la Recherche (ANR)  under contract  ANR-2010-BLAN-609-01  and by the  region Champagne-Ardenne in France. The authors are grateful for the logistical and infrastructural support provided by  IPMet/UNESP  under a collaborative agreement with the French CNRS. We also thank IPMet's technicians,  Hermes França ,  Bruno Biazon  and  Demilson Quintão , for their assistance during the TRO-Pico campaigns and  Joseph T. Hodges  from  NIST  for his greatly appreciated help reviewing this paper. Edited by:  M. von Hobe  www. atmos-meas-tech. net/9/1207/2016/Atmos. Meas. Tech. , 9,  1207-1219 , 2016.
Nonetheless, as far as the AGB estimation is concerned, the cutting edge remote techniques are still highly dependent on the availability, quality and quantity of traditional local-scale field inventory systems  [3]. Briefly, AGB is assessed from remote sensing data through a two-stage procedure. At the first stage, remote sensing measures or "metrics" are related to AGB, typically via regression models, with associated field-derived AGB. At the second stage, the resulting AGB regression models are used to convert local-scale estimates into continuous AGB maps over larger areas  [4, 5]. The success of this approach strongly depends on the amount and quality of field-derived AGB. In most national and regional inventories, the plots are small in size and have significant uncertainty in location that may introduce large errors in calibrating the plot-level lidar metrics.
), ferns and herbaceous plants. We first present the study site, which is covered by a Portuguese multilayered forest (Section 2. 1). Secondly, we explain the approaches used to acquire forest metrics using either field sampling techniques (Section 2. 2) or lidar data processing methods (Section 2. 3). The AGB is estimated over 40 forest plots by means of three different techniques: (1) using field inventory measurements together with a set of allometric equations developed to apply to Portuguese forests (Section 2. 4); (2) using lidar-derived forest metrics and the very same set of allometric equations (Section 2. 5); and (3) using the traditional approach that establishes AGB regression models using both field inventory measurements and lidar point cloud metrics (Section 2. 6). Regarding both (  1 ) and (  2 ), we first compare results for single layers (mature overstory, juvenile overstory, understory and ground vegetation; Section 3.