dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The contribution of K. B. Rodgers came through awards  NA17RJ2612  and  NA08OAR4320752 , which includes support through the  NOAA Office of Climate Observations (OCO). The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of NOAA or the  US Department of Commerce. Additional support for  K. B. Rodgers came  through  NASA  award  NNX09AI13G  and  NSF  award  OCE-1155983. Support for S. E. Mikaloff Fletcher  was provided by  NIWA  under the  New Zealand Greenhouse Gas Emissions, Mitigation , and  Carbon Cycle Science Programme. Y. Plancherel  acknowledges support from the  Oxford Martin School  and the  UK GEOTRACES Program. Support for measurements of APO ratios was provided under grant  NA10OAR4320156 , which includes support through the  NOAA Climate Program Office.
The ''oasis hypothesis'' supports also the idea of higher species richness in seamounts compared to the surrounding deep-sea ecosystems  [2, 12]. Birds, mammals, turtles, fish and top pelagic predators are usually represented by high diversity over seamounts  [13]. It is unclear, however, if increased habitat heterogeneity and complexity, for example due to the presence of biogenic structures, result in an elevated benthic diversity too  [1]. The few studies available government under the program ''Investissements d'Avenir'' by a grant from the Regional Council of Brittany (SAD programme), and by the project ''Deep-sea hYdrothermal Vent nematodes as potential source of new Antibiotics'' (DYVA) funded by Fondation Total and Ifremer. LB was supported by the RITMARE flagship project 2012-2016 funded by MIUR and coordinated by CNR, Italy.
In order to facilitate the comparison among samples the expected number of nematode species for a theoretical random sample of 100 individuals, ES (100), was calculated. All indices were calculated using PRIMER6 software (Plymouth Marine Laboratory, UK  [41] ). We also measured the turnover among samples (b diversity). The b diversity provides indications of any change in species composition among the samples  [16]  and can be expressed as percentages of dissimilarity of nematode community species composition (e. g. calculated on a Bray-Curtis similarity matrix  [42] ). The SIMPER analysis was used to determine the contributions of each species to the average Bray-Curtis dissimilarity  [43]. Before the analysis, the diversity matrix was square root transformed. The trophic diversity of the nematodes was determined by analysis of the trophic groups, as reported by Wieser  [44].
Statistical analyses Differences in nematode abundance, biomass and biodiversity among sites (including seamount and in the external area) were tested by a one-way analysis of variance (ANOVA). The GMAV (1997) statistical package (University of Sydney, Australia) was used to perform the ANOVA. Before the ANOVA, the homogeneity of variances was tested using the Cochran test and data were appropriately transformed whenever necessary. For those data which transformation did not allow a homogenization of variance, we adopted a more conservative level of significance. The Student Newman-Keuls (SNK) test was used for post hoc comparisons. Bray-Curtis similarities among all of the sampling sites (with data fourth root transformed), the analysis of the similarities (ANOSIM) and the similarity percentages (SIMPER) were performed with the PRIMER6  [41].
These statistical analyses were carried out to measure the similarities in the nematode specie composition among all of the investigated samples (24 replicates: 6 sites X 4 replicates). To evaluate the relationship between nematode abundance, biomass and species composition and environmental variables (i. e. water depth, sediment grain size as indicator of habitat heterogeneity, and trophic characteristics of sedimentary organic matter as proxy of food availability for benthic consumers) we conducted a non-parametric multivariate multiple regression analysis (DistLM: distancebased linear model) using the PERMANOVA + add-on package for PRIMER6 software  [47, 48]. This method analyses and models the relationship between a multivariate data cloud, and one or more predictor variables. It is based on a resemblance matrix and uses permutations, rather than the restrictive Euclidean distance and normality assumptions which underlie the standard approach to linear modelling.
Acknowledgments We would like to thank the  CONDOR  project ( Observatory for long term study and monitoring of Azorean  ecosystem, EEA Grants  PT0040/2008 ,  Iceland, Liechtenstein)  and in particular  Dr. Gui Menezes  and  Dr. Eva Giacomello. We thank captain and crew of the R. V. Noruega , and cruise participants from  IMAR-DOP/University of the Azores, IPIMAR, ISMAR-CNR (Italy)  for their valuable help during sediment sampling. We thank the  Fundaca ˜o para a Ciencia e Tecnologia, Portugal  ( FCT  Project  BIOMOUNT, PTDC/MAR/105486/2008 ,  COMPETE/FEDERE  funds). IMAR-DOP/UAz is Research and Development Unit no. 531  and  LARSyS-Associated Laboratory  no. 9 funded by the  Portuguese Foundation for Science and Technology (FCT)  through  FCT -Pest/OE/EEI/ LA0009/2011-2014  and by the  Azores Fund for Science and Technology (FRCT) , funded by  OE ,  COMPETE , QREN and ProConvergencia.
We also thank  FCT -Pest/OE/EEI/LA0009/2014  ( OE, COMPETE and QREN ) for support. DZ was partly supported by the '' Laboratoire d'Excellence'' LabexMER  ( ANR-10-LABX-19 ), a grant from the  French government  under the  program ''Investissements d'Avenir' ' and from the  Regional Council of Brittany  (SAD programme), and by the project '' Deep-sea hYdrothermal Vent nematodes as potential source of new Antibiotics (DYVA)  funded by  Fondation Total and Ifremer. LB was supported by the  RITMARE  flagship project  2012-2016  funded by  MIUR  and coordinated by  CNR, Italy.
This study was funded by the  CONDOR  project ( Observatory for long term study and monitoring of Azorean  ecosystem, EEA Grants  PT0040/2008 , ( Iceland, Liechtenstein) , the  Fundaca ˜o para a Ciencia e Tecnologia, Portugal  ( FCT  Project  BIOMOUNT, PTDC/MAR/105486/ 2008 ,  COMPETE/FEDERE  funds). IMAR-DOP/ UAz is Research and Development Unit no. 531  and  LARSyS-Associated Laboratory  no. 9 funded by the  Portuguese Foundation for Science and Technology (FCT)  through  FCT -Pest/OE/EEI/ LA0009/2011-2014  and by the  Azores Fund for Science and Technology (FRCT) , funded by  OE, COMPETE, QREN and ProConvergencia. The Open Access of this paper is funded by  FCT -Pest/ OE/EEI/LA0009/2014  (OE, COMPETE and QREN). DZ was partly supported by the '' Laboratoire d'Excellence'' LabexMER  ( ANR-10-LABX-19 ), co-funded by a grant from the  French.
Acknowledgments This work was supported by the following grants:  French Ministry of Research (MENRT)  grant to  Auré lie Lelong , grant from the  Region Bretagne  ( SAD Volet 1: PSEUDOPE ) to Hé lè ne Hé garet and a  CNRS EC2CO Microbien  grant to Philippe Soudant and the  Agence Nationale de la Recherche  (ANR; Iron, Copper and  Oceanic Phytoplankton ,  ICOP ,  ANR-10-JCJC-606 ) to  Eva Bucciarelli. The CLNN-16 strain of P. multiseries was kindly provided by  Nancy Lewis. The authors thank  Gary H. Wikfors  for participating in preliminary experiments associated with this study and  Gary H. Wikfors  and  Stephen S. Bates  for manuscript improvements and English corrections.
Acknowledgements. The authors acknowledge the  ICMAT Severo Ochoa  project  SEV-2011-0087  for covering the publication costs. A. Carrassi  was financed through the  IEF Marie Curie Project  INCLIDA of the  FP7. R. J. T. Weber  is supported by the  PROMOS  grant of the  DAAD (German Academic Exchange Service ). This work was supported by the  EU- funded  SPECS  ( FP7-ENV-2012-308378 ) and the  MINECO-funded PICA-ICE  ( CGL2012-31987 ) projects. Edited by: J. Duan Reviewed by: three anonymous referees.
In this study, we investigate the ability of the chemistry transport model (CTM) POLAIR3D of the air quality modelling platform POLYPHEMUS to simulate lidar backscattered profiles from model aerosol concentration outputs. This investigation is an important preprocessing stage of data assimilation (validation of the observation operator). To do so, simulated lidar signals are compared to hourly lidar observations performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009, when a ground-based mobile lidar was deployed around Paris onboard a van. The comparison is performed for six different measurement days, 1, 4, 16, 21, 26 and 29 July 2009, corresponding to different levels of pollution and different atmospheric conditions.
Overall, POLYPHEMUS well reproduces the vertical distribution of lidar signals and their temporal variability, especially for 1, 16, 26 and 29 July 2009. Discrepancies on 4 and 21 July 2009 are due to high-altitude aerosol layers, which are not well modelled. In the second part of this study, two new algorithms for assimilating lidar observations based on the optimal interpolation method are presented. One algorithm analyses PM 10 (particulate matter with diameter less than 10 µm) concentrations. Another analyses PM 2. 5 (particulate matter with diameter less than 2. 5 µm) and PM 2. 5-10 (particulate matter with a diameter higher than 2. 5 µm and lower than 10 µm) concentrations separately. The aerosol simulations without and with lidar data assimilation (DA) are evaluated using the Airparif (a regional operational network in charge of air quality survey around the Paris area) database to demonstrate the feasibility and usefulness of assimilating lidar profiles for aerosol forecasts.
The most frequently used data are in situ surface measurements, e. g. AirBase (http://www. eea. europa. eu/) and EMEP over Europe, BDQA (Base de Données de la Qualité de l'Air)  (Sartelet et al. , 2007; Konovalov et al. , 2009). However, they do not provide direct information on vertical profiles. Satellite passive remote sensors (e. g. the Moderate Resolution Imaging Spectroradiometers -MODIS) and sunphotometer surface stations (e. g. the AErosol RObotic NETwork -AERONET) have greatly enhanced our ability to evaluate such models. Comparisons between observed and simulated aerosol optical depth (AOD) have been performed for global models and regional models  (Kinne et al. , 2006; Tombette et al. , 2008; Péré et al. , 2010). However, instruments such as sun photometers can only retrieve columnintegrated aerosol properties and can only work during daytime.
Modelling setup and observational data The modelling domain is the same as the one used in  Royer et al. (2011)  and  Couvidat et al. (2013). It covers the Greater Paris area  Royer et al. (2011)  show that limited vertical model resolution leads to much smoother vertical profiles than those deduced from lidar signals, a finer vertical resolution is used with 23 vertical levels from the ground to 12 000 m, instead of the 9 vertical levels in  Royer et al. (2011). The simulations are carried out for 1 month, from 28 June to 30 July 2009. Meteorological inputs are the same as in  Couvidat et al. (2013). They are simulated with the Weather Research and Forecasting (WRF) model  (Skamarock et al. , 2008)  using an urban canopy model and an undated Corine land-use database  (Kim et al. , 2013)  with the Yonsei University (YSU) parameterisation  (Hong et al.
asso. fr/). It provides hourly gas and/or aerosol concentrations (PM 10 and PM 2. 5 ) measurements. Figure  1  shows the location of the Airparif stations by means of red squares and/or black triangles. There are 17 stations at which PM 10 and/or PM 2. 5 concentration measurements are performed. The AERONET programme is a federation of groundbased remote sensing aerosol networks established by NASA and PHOTONS (Univ. of Lille 1, CNES and CNRS-INSU), which provides a long-term, continuous and readily accessible public-domain database of aerosol optical measurements performed by sun photometers  (Holben et al. , 1998)  photometers measure AOD at different wavelengths ranging from 340 to 1024 nm. AOD data are computed for three data quality levels: level 1. 0 (unscreened), level 1. 5 (cloud-screened), and level 2.
On 16 July 2009, GBML measurements are performed in the north of Paris from Saclay to Amiens between 11:00 and 14:30 UTC. The lidar signal is underestimated everywhere, as shown in Fig. 6 , in agreement with the underestimation of PM 10 shown by the statistics in Tables  2  and 3. Surface PM 10 concentration from the Airparif network and from the simulation are 26. 25 and 16. 47 µg m -3 respectively (lowmoderate level of pollution; see Table  2 ). The observed and simulated AOD are 0. 26 and 0. 18 respectively (see Table  3 ). The simulated AOD has a good correlation with AERONET data (up to 80 %). As deduced from the comparisons of the modelled and observed lidar signals in Fig. 6 , the PBL height is well modelled until 12:00 UTC, but it is underestimated afterwards; for example, the PBL height is about 2.
Assimilation test of lidar observations DA of lidar observations is performed for two out of the six different measurement days. Only these two days are retained due to the other days being cloudy and our algorithms not allowing us to assimilate lidar data when there are clouds. There is 13 h of cloud-cleaned measurements on 1 July, 5 h of cloud-cleaned measurements on 26 July and less than 3 h of cloud-cleaned measurements on the other measurement days. Therefore, DA run is performed on 1 and 26 July 2009 because too few data are available during the other measurement days. In air quality, the large number of state variables leads to high computational costs when implementing DA algorithms. Among the widely used DA algorithms, the optimal interpolation (OI) is used here, as it is the most computationally efficient  (Denby et al.
Acknowledgements. This work was supported by  CEA  ( Commissariat à l'Énergie Atomique ) and  CEREA, Joint Laboratory École des Ponts ParisTech -EDF  R & D. This paper is also a contribution to the INSU/LEFE ADOMOCA-2 project. The authors thank their colleague  Florian COUVIDAT  for help with performing the simulation used in this paper. The authors would like to thank  Philippe ROYER  and  Jean-Christophe RAUT  for their contributions to the MEGAPOLI summer campaign. Edited by:  J. Brandt  The publication of this article is financed by  CNRS-INSU.
The tropical deep overshooting convection is known to be most intense above continental areas such as South America, Africa, and the maritime continent. However, its impact on the tropical tropopause layer (TTL) at global scale remains debated. In our analysis, we use the 8year Microwave Limb Sounder (MLS) water vapour (H 2 O), cloud ice-water content (IWC), and temperature data sets from 2005 to date, to highlight the interplays between these parameters and their role in the water vapour variability in the TTL, and separately in the northern and southern tropics. In the tropical upper troposphere (177 hPa), continents, including the maritime continent, present the nighttime (01:30 local time, LT) peak in the water vapour mixing ratio characteristic of the H 2 O diurnal cycle above tropical land.
Methodology Eight boxes of 10 • latitude × 20 • longitude have been created over southern tropical South America (0-10 • S, 55-75 • W), Africa (0-10 • S, 15-35 • E), the maritime continent (0-10 • S, 110-130 • E), the western Pacific (0-10 • S, 150-170 • E), and over northern tropical South America (0-10 • N, 55-75 • W), Africa (0-10 • N, 15-35 • E), the maritime continent (0-10 • N, 110-130 • E), and western Pacific (0-10 • N, 150-170 • E) (as represented in the upper-right panel of Fig. 2 ). Note that we will refer hereafter to northern and southern tropics as the (0, 10 • N) and (0, 10 • S) latitudes, respectively. The MLS H 2 O v3. 3 data set has been averaged monthly within each box from January 2005 to December 2012. In order to better focus on seasonal cycles, the semiannual oscillation (SAO)  (Delisi and Dunkerton, 1988 ) and the inter-annual variability, (such as that related to the quasibiennial oscillation (QBO) or the El Niño-Southern Oscillation (ENSO),  Liang et al.
Acknowledgements. The work was supported by the  French Agence Nationale de la Recherche (ANR)  TRO-pico project (http://www. univ-reims. fr/TRO-pico/). The data used in this effort were acquired as part of the activities of  NASA's Science Mission Directorate , and are archived and distributed by the  Goddard Earth Sciences (GES) Data and Information Services Center (DISC). We are grateful to the referees for their comments and suggestions that helped us to substantially improve the quality of our study. Edited by:  P. Haynes.
g. ,  Einarsson, 1991; Roman et al. , 2008; White et al. , 2011). Thus, a second stress analysis was carried out to evaluate the state of stress during a volcanic crisis and the results were compared to the regional stress field. In this paper, a regional stress field that includes (1) a checkerboard test to evaluate the sensitivity of random choice of the nodal plans of double focal solutions and the confidence in the dimension of the selected subareas,  (2)  an analysis of the different damped stress fields obtained by the  Hardebeck and Michael (2006)  approach in terms of stress orientations and stress regime, (3) the stability of the resulting stress field by an analysis of random errors propagated in the data is carried out over a 20 year time-span. Another "local" stress inversion is effected during a volcanic crisis using a random stress search.
Red dots: active central volcanoes belonging to the volcanic systems. B: Bárðarbunga; D: Dyngjufjöll-Ytri; G: Grimsvötn; H: Hamarinn; Ha: Hagöngur; K: Kverkfjöll; Ka: Katla; P: Þórðarhyrna; V: Vonarskarð. (B) Area of study showing the epicenters of the dataset used for the inversion (2434 mechanisms) as function of depth, and the volcanic systems. 1:fissure swarm; 2: domain of central volcano; 3: caldera (after Jóhannesson and  Saemundsson, 1998). to interactions between tectonic and volcanic processes, intimately linked in such a region. On the Earth's surface, these interactions are expressed by volcano-tectonic systems made of central volcanoes, long eruptive and non-eruptive fissures and faults  (Saemundsson, 1979). Moreover, Iceland is undergoing glacial isostatic adjustment due to the recent retreat of the glacier  (Árnadóttir et al.
MATERIALS AND METHODS Focal mechanisms are commonly used to investigate the crustal stress in tectonically active regions. However, determining the stress field from focal mechanisms is not straightforward and raises several questions: what is the volume in which crustal stress can be considered to be homogeneous? What is the number and quality of the required data? How can the presence of different types of focal mechanisms within the volume and the geometry of the active tectonic systems be explained? Paleostress analyses highlighted that the separation of a heterogeneous dataset into spatially and temporally homogeneous subsets is fundamental to avoid artefacts in stress solutions: such computed solutions can only represent a compromise between different homogeneous subsets and hence are inconsistent with the actual stress field (e.
g. ,  Célérier et al. , 2012). Automatic and interactive separations to obtain homogeneous subsets have been performed for paleostresses (e. g. ,  Armijo and Cisternas, 1978; Angelier, 1979; Angelier and Manoussis, 1980; Etchecopar et al. , 1981; Etchecopar, 1984; Mercier and Carey-Gailhardis, 1989; Yamaji, 2000 Yamaji, , 2003)). However, such automatic methods may give rise to serious artefacts  (Angelier, 1984; Lisle and Vandycke, 1996; Delvaux and Sperner, 2003; Liesa and Lisle, 2004; Sperner and Zweigel, 2010). The most robust way forward is to separate the data based on fault kinematics, mechanical consistency and field observations (e. g. ,  Bergerat, 1987) , but this is not feasible when the dataset consists of hundreds or thousands of focal mechanisms. Hardebeck and Michael (2006)  proposed a stress inversion method that finds the least complex stress field model consistent with the data.
THE EARTHQUAKE CATALOG USED FOR INVERSION In the area studied, 5997 earthquakes with moment magnitudes from -2. 3 to 5. 4 M w were recorded in the IMO database from 1991 to 2009 and each of these events was associated with a double-couple focal mechanism. An estimation of the fault plane solution is based on P and S polarities and amplitude data  (Rögnvaldsson and Slunga, 1993, 1994). Fault plane solutions and earthquake location are refined interactively by checking the arrival times of seismic events issued from the network routine  (Böðvarsson et al. , 1996). Single-event and multi-event locations are used in the routine analysis. Where the station coverage is good, the optimal fault plane solution can be expected to be within ±15 • of the three source angles for events not larger than 0.
(1981)  and  Etchecopar (1984)  implemented in the Fsa package  (Célérier, 2013) , which minimizes the angular α misfit between τ and s. For more details pertaining to the minimization criteria, the reader is referred to the review of  Célérier et al. (2012). The first algorithm is used to determine the regional stress field using all the dataset. The second is used to detect the mechanically homogenous data subset when less data are considered for inversion, such as during a volcanic eruption. In this way, the compatibility of the stress tensor can be verified with each datum. For double-couple focal mechanisms, an ambiguity of the two nodal planes arises because the shear stress (τ ) and the slip (s) cannot be aligned on both nodal planes, unless a principal stress axis is parallel to the B axis or the stresses are axially symmetric such that is close to 0 or 1  (Gephart, 1985).
DAMPED STRESS INVERSION We argue that the stress field is a function that can vary gradually from one point to another due to local forces superimposed on the regional tectonic stress. To take into account this progressive variation, we assume that the stress tensor computed for the area just around the point of coordinates x, y, and z is due to mechanical local conditions existing at this point, but is also influenced by tectonic conditions of the neighboring volume (x ± ε, y ± ε, z ± ε). The region studied is divided into a series of cells and the dataset (in our case the focal mechanism parameters) is partitioned in this mesh. In each cell, if the number of data is sufficient, it is theoretically possible to invert the local stress tensor as explained above. We invert simultaneously all the tensors for all the cells using the classical equation of tomography techniques given by: G all m all = d all (2) Where d all is the data matrix (slip vector and plane orientation for each focal mechanism) and m all the model matrix containing the stress tensors which is defined by 5 parameters (see details in  Hardebeck and Michael, 2006).
RANDOM STRESS SEARCH The random stress search is based on a Monte Carlo procedure and was developed for stress inversion by  Etchecopar et al. (1981)  and  Etchecopar (1984). In this study, we used the Fsa package  (Célérier, 2013). The best reduced stress tensor is obtained by testing a number of random stress tensors (here 10,000) and selecting those that yield the shear stress direction as close as possible to the observed slip vector of a data subset. The individual angular α misfit is defined as the absolute difference between the observed and predicted rake for each focal mechanism. The misfit function is thus defined by the average of the lowest individual misfit of the dataset.