dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the gap in refracted arrivals from the pre-Messinian units in the OBS data records does not allow to quantify the depth and the velocity of this layer. The RMO gathers are a key to define the pre-Messinian velocities and the depth of the top of the basement: we define the bottom of the pre-Messinian unit as the zone where flattened reflected events disappear (Fig. 4c ). RMO gathers are flattened with velocities ranging from 2. 95 to 3. 05 km s -1 at the top and 3. 5-3. 65 km s -1 at the base of the pre-Messinian unit. The deep part of the model (crust and mantle) is solely constrained by the analysis of OBS records, as the MCS data does not exhibit many coherent seismic reflections from the crust and from the Moho.
The wide-angle profile is located on the western boundary of the basement which extends eastward over 100 km  (Bouillin 1977). At this location, the Lesser Kabylia basement limit is oblique to the margin and may therefore be almost vertical. Finally between 210 and 240 km, the model incorporates an approximately 3 km thick basin between land stations LS07 and LS11. At the surface, this zone was interpreted as Tellian nappes domain (Fig. 1 ). However, in the absence of seismic data characterizing the unit composing the suture, its precise structure remains undefined.
C O N C L U S I O N S The onshore-offshore combined modelling of wide-angle OBS and LS data and pre-stack depth migration of the seismic reflection data provides an unprecedented image of the deep structure along a 280 km long transect across the Algerian deep sea basin and continental margin. The Lesser Kabylian continental crust presents a sharp subvertical contact with the external zones. There, its thickness reaches 26 km. The crust thins to less than 5 km within less than 70 km: The continental shelf is narrow and has a very steep slope while the lower crust thins more seaward, suggesting an asymmetric and depth dependent thinning. Seaward in the deep sea basin, the crustal velocities evolve to two distinct layers of 4. 7-6. 1 and 6. 2-7. 1 km s -1 , typical of oceanic basins. The ocean-continent transition zone is very narrow, between 15 and 20 km.
A C K N O W L E D G E M E N T S We are truly grateful to the captain and crew of the N/V l'Atalante. We wish to thank also the OBS team of Ifremer-UBO and the Genavir seismic team for their dedication at sea. We wish to acknowledge SONATRACH (National oil company, Algeria) and AL-NAFT (Agence Nationale pour la Valorisation des Ressources en Hydrocarbures, Algeria) to allow us to use and publish the additional seismic lines. The MCS profiles were processed using the Geocluster (CGG Veritas), and Seismic Unix  (Stockwell 1999a,b)  processing software. The GMT (Generic Mapping Tool) software was used in the preparation of most figures  (Wessel & Smith 1995). The Rayinvr software  (Zelt & Smith 1992)  was used for ray tracing modelling of the wide-angle seismic data.
Automatic picking method The automatic picking method presents as follows. As first step, a reference reflector, typically the glacier bed, was manually picked in ReflexW (Version 6. 1. 1, by Sandmeier Software). The automatic picking method then works up from the respective reference reflector (Fig. 2 ). As long as this reference reflector is within the temperate ice layer, it does not affect the automatic CTS pick. However, if the reference reflector coincides with the glacier bed, it then allows the bonus of calculating the thickness of the temperate ice layer. On Midtre Lovenbreen, glacier bed and reference reflector coincided, whereas on the other two test sites an arbitrary level within the temperate ice was used, as the GPR did not penetrate deep enough to record the glacier bed. In the following, both starting-point methods are referred to as a reference reflector.
However, if not stated otherwise, a window length of 28 ns was used. In the moving window, the percentage of signal power values above P signal was calculated (PC mov ). As long as PC mov ! PC sample , the moving window was moved vertically upward by one sample in the radar time trace. When PC mov < PC sample , the lowest point in the moving time window was marked as the CTS and the algorithm moved on to the next trace. This process was carried out for all traces in the radargram. Finally, in order to reduce the effect of miscomputed CTS depths on the final CTS pick on each trace, we used the 'smooth' function in MATLAB 1 (http://www. mathworks. co. uk/help/curvefit/smooth. html). Several smoothing methods are available within this function. In this study, a local regression filter worked best (Fig.
2 ). The span of the local regression filter was 20 traces ($10 m lateral distance) for all radargrams. The filter assigns regression weights to each of the computed CTS depths in the span. The highest regression weight is always assigned to the trace to be smoothed. All CTS depths outside the filter span are assigned zero regression weights. In the filter span, a linear-least-squares regression is carried out. The local regression filter was used in the robust mode. As well as calculating the regression weights, this mode determines the robust weights in the filter span. These robust weights are resistant to outliers, and therefore considerably improved our final CTS pick. For a more detailed description of the smoothing function, the reader is referred to the MATLAB 1 Help.
In this case, the automatic pick tends to underestimate the CTS (Fig. 4d ). A general trend throughout the dataset was that the best fit between the automatic and manual picking methods was observed in areas where the CTS depth did not show much lateral variation (Figs  3d  and 4d ). Mean RMSEs calculated in two-way travel-time (TWTT) at every trace in each radargram lie in a very similar range for all three test sites. This indicates that the use of different radar systems does not significantly affect the uncertainty of the automatic picking method. In general, a larger spread in mean RMSEs was observed on Midtre Lovenbreen than on Storglacia ¨ren. The RMSE observed on Midtre Lovenbreen ranged from 9. 7 to 32. 2 ns (1. 6-5. 4 m), whereas on Storglacia ¨ren the RMSE only varied from 10. 5 to 16.
ACKNOWLEDGEMENTS We thank  Jean-Michel Friedt  for acquiring, and providing us with, the 2011 data from Midtre Lovenbreen. The work presented in this paper was supported by  UK Natural Environment Research Council (NERC)  grants  GR3/R9757 ,  NER/A/S/2002/01000 ,  NER/S/A/2004/121333  and  ARCFAC 026129. C. S. was partly funded by a  Swansea University scholarship. A. G. is supported by the  Alaska Climate Science Center , funded by Cooperative Agreement No. G10AC00588  from the  United States Geological Survey. We thank an anonymous reviewer,  Svein-Erik Hamran  and  Francisco Navarro  for comments which greatly improved the manuscript.
Incidentally,  Meredith et al. [2012]  recently compared Cluster, THEMIS, and CRRES wave amplitudes B w (similarly deduced from E w ) near L = 6 and found Cluster and THEMIS measurements to agree well, while CRRES amplitudes on the nightside appeared generally larger than THEMIS ones by factors ∼ 2. For the sake of consistency with the data from Cluster used throughout the present study, CRRES RMS amplitudes B w have therefore been divided by 2 in order to roughly coincide with Cluster measurements of magnetic field fluctuations. Some possible explanations for this discrepancy stem from (1) the fact that the CRRES database covers only 1 year in 1990-1991 with more nightside coverage  [Spasojevic and Shprits, 2013; Horne et al. , 2013] , while the Cluster database covers almost a full solar cycle in 2001-2011 with more dayside coverage, and (2) the likely overestimation of the actual value of B w in CRRES fits  [Spasojevic and Shprits, 2013]  as their assumption B w ∕E w = N =0 (with N the cold plasma refractive index) becomes erroneous for  > 20 • [see  Lengyel-Frey et al.
EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989-2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent.
As part of this effort, model evaluation activities in the individual modeling centers are harmonized and a new generation of regional climate projections for land regions worldwide based on new CMIP5 (Coupled Model Intercomparison Project) GCM projections will be produced. First joint evaluations of CORDEX RCM experiments have recently been published by  Nikulin et al. (2012)  and  Vautard et al. (2013b). EURO-CORDEX, the European branch of CORDEX  (Jacob et al. , 2014 ), provides regional climate projections for Europe at grid resolutions of about 12 and 50 km, applying an ensemble of RCMs in their most recent versions, driven by the latest GCM projections, thereby complementing the already available PRU-DENCE and ENSEMBLES data with unprecedented high resolution experiments. In its initial phase EURO-CORDEX focuses on model evaluation for present-day climate in a perfect boundary setting.
Although simulations carried out at grid resolutions of both 12 and 50 km are analyzed, we do not specifically aim to investigate the added value of a higher resolution. This would require reliable observation-based data sets at the European scale with equivalent resolution, which are not available. Added value assessments are therefore allocated to a suite of accompanying studies evaluating aspects such as extreme precipitation characteristics over subdomains of the European continent where corresponding reference data exist (see Sect. 5. 1 for further details). The primary aims of the present study are (1) to document the skill of the EURO-CORDEX RCM ensemble in reproducing the present-day European temperature and precipitation climate when driven by realistic boundary conditions, (2) to quantify modeling uncertainties originating from model formulation, (3) to assess a possible progress with respect to the precursor project ENSEMBLES, and (4) to highlight areas of necessary model improvements.
For this purpose, we will apply several evaluation metrics covering a range of aspects of model performance. Our study provides a general overview on model performance and is of rather descriptive nature; it does not aim to ultimately explain biases of individual models. We leave these more-detailed investigations to a range of follow-up studies that will address specific aspects of model performance. The study is organized in the following way: after introducing the RCM ensembles and the observational reference data in Sect. 2, Sect. 3 outlines the evaluation methods applied and introduces the individual performance metrics. Section 4 then presents the evaluation results for the EURO-CORDEX ensemble and relates them to the previous EN-SEMBLES experiments. The results are further discussed in Sect.
RCM data We evaluate a set of 17 RCM simulations carried out in the frame of EURO-CORDEX. In total, six different RCMs plus the global ARPEGE model were applied by nine different institutions at grid resolutions of about 12 km (0. 11 • on a rotated grid) and 50 km (0. 44 • on a rotated grid). Eight out of the nine 0. 11 • experiments have a corresponding partner at 0. 44 • grid spacing, carried out with the identical model version and the identical choice of parameterizations (with the exception of REMO, where rain advection is used for the 0. 11 • experiments but not for 0. 44 • ). All simulations cover the period 1989-2008 and are driven by the ERA-Interim reanalysis  (Dee et al. , 2011) , providing the required atmospheric lateral boundary conditions and sea surface temperatures and sea ice cover over ocean surfaces.
Observations As observational reference for evaluating simulated temperature and precipitation we use version 7 of the daily gridded E-OBS data set  (Haylock et al. , 2008). E-OBS covers the entire European land surface and is based on the ECA&D (European Climate Assessment and Dataset) station data set plus more than 2000 further stations from different archives. It is available at four different resolutions; we here use the rotated 0. 22 • version, which applies the same grid rotation as most of the EURO-CORDEX and ENSEMBLES experiments. The E-OBS 0. 22 • grid corresponds to a horizontal resolution of about 25 km and exactly matches the grid of the 0. 22 • ENSEMBLES simulations. Each E-OBS 0. 22 • grid cell contains four cells of the rotated 0. 11 • EURO-CORDEX grid, and four E-OBS 0. 22 • cells exactly match one rotated 0.
Regridding Several evaluation metrics require a grid-cell-by-grid-cell comparison between models and observations. Consequently, a remapping of either the EURO-CORDEX RCM output or of E-OBS to a common reference grid was necessary prior to the analysis. In order to ensure a fair evaluation, our strategy was to always use the coarser grid as reference, except for mean sea-level pressure (see below). This means that (1) the evaluation of the EUR-11 ensemble was carried out on the coarser 0. 22 • E-OBS grid, and that (2) the EUR-44 experiments were evaluated on their native 0. 44 • model grid. In the first case, the model data were conservatively projected onto the 0. 22 • E-OBS grid. In the second case, E-OBS 0. 22 • was conservatively projected onto the 0. 44 • model grid (rather than directly applying the rotated 0.
Data access Since 1 October 2013, the simulations of the EUR-11 and EUR-44 ensembles are being published and distributed via the Earth System Grid Federation (ESGF) under the project name "CORDEX". This includes the ERA-Interim driven experiments and the GCM-driven climate change scenarios. Data can be accessed via several ESGF data nodes, such as esgf-data. dkrz. de, esgf-index1. ceda. ac. uk, cordexesg. dmi. dk, esgf-node. ipsl. fr, and esg-dn1. nsc. liu. se. Note that the ESGF archive is subject to continuous extensions and that the EUR-11 and EUR-44 ensembles available in this archive may not cover the whole ensemble presented in this article at the time of access or may contain more experiments. For a complete list of EURO-CORDEX simulations, please refer to the EURO-CORDEX homepage (www.
Acknowledgements. Calculations for WRF IPSL-INERIS were made using the TGCC super computers under the  GENCI  time allocation GEN6877. The contribution from CRP-GL was funded by the  Luxembourg National Research Fund (FNR)  through grant  FNR C09/SR/16  (CLIMPACT). The KNMI-RACMO2 simulations were supported by the  Dutch Ministry of Infrastructure  and the Environment. The CCLM and REMO simulations were supported by the  Federal Ministry of Education and Research (BMBF)  and performed under the Konsortial share at the  German Climate Computing Centre (DKRZ). The CCLM simulations were furthermore supported by the  Swiss National Supercomputing Centre (CSCS)  under project  ID s78. The contribution from UHOH was funded by the  German Science Foundation (DFG)  through project  FOR 1695. Part of the SMHI contribution was carried out in the Swedish Mistra-SWECIA programme founded by  Mistra (the Foundation for Strategic Environmental Research).
The current field Despite the history and success of the field of atmospheric chemistry having such roots, the ability of the atmospheric chemistry community to deliver such fundamental insights and data is at a crossroads. In particular, resources are being increasingly allocated in a targeted manner to applied-or solution-oriented science, such as that proposed within the Future Earth initiative (ICSU), with the underpinning, fundamental work de-emphasized. As well, a large emphasis is given to modeling necessary for international assessments. Yet, these models are only as good as the fundamental chemistry on which their code is based and whether it is evaluated against laboratory and ambient observations. The de-emphasis of fundamental atmospheric chemistry may be due in part to the multi-disciplinary nature of atmospheric chemistry that is an amalgam of engineers, physicists, meteorologists, biologists, computer scientists, and chemists.
Study population The study population was recruited in 2010 from the 14 sites with teams of case managers. One site had only one case manager, and this site was excluded because the case manager was newly recruited and lacked experience as a case manager. All the teams were multidisciplinary, with at least one nurse at 14 sites, one social worker at 12 sites, one psychologist at 6 sites and one occupational therapist at 4 sites. Three case managers had legal backgrounds and were classified in the "other" group. The case managers worked for at least six months following their recruitment. Whatever their initial backgrounds, all the case managers assumed only the role of case manager at their site. All the case managers were approached to participate in the study. One case manager refused to participate and five were not available on the day that the data was collected.
Data collection Qualitative data were collected at each site by holding focus groups. Focus groups served as our main source of data. One focus group was conducted per site, with participation by the entire team of case managers working at this site. The decision was made to form focus groups with all the professional case managers, independent of their original professions. The focus groups were held at each site in a quiet space where the case managers practised. All the focus groups were conducted by the same person, a public health physician who was trained in qualitative methods. The focus groups lasted between 120 and 180 minutes. An interview guide was developed based on the current literature on case management. The literature helped us address unanswered questions concerning the role played by a team of case managers.
The resulting interview guide was refined in the field following an initial pilot interview. The interview guide began with a very general question on professional practices and then moved on to more specific questions designed to explore the implementation of case management: (1) participants' motivations for becoming a case manager, (2) the activities of case managers, (3) the team of case managers and partnering, (4) the implementation of case management (see Additional file 1). A short anonymous questionnaire was administered following the focus group. It provided socio-demographic data and information on individual perceptions of the multidisciplinary team of case managers. To complement the focus group, two researchers relied on non-participant observations and spent time at various community-based health services in order to observe key professionals' practices and information exchange.
Coding and analysis Transcripts were produced, read and coded by two of the researchers. The coding was validated by another researcher, who used a consensus approach to resolve discrepancies  [25]. The coding and analysis was performed using the Strauss and Corbin grounded theory approach, consisting of open, axial and selective coding  [26]  in order to identify relevant categories and relationships. Nvivo8 software was used to code the data and a standardized method of qualitative analysis was applied. We began with a round of open coding of the interviews focused on issues pertaining to relationships. The codes were descriptive, based on words used by participants such as "colleague," "background," and "difficulties. " Then, following the application of an axial coding strategy  [26] , codes with the same content and meaning were grouped into categories (e.
A meta-analysis was conducted to evaluate the effects of protozoa concentration on methane emission from ruminants. A database was built from 59 publications reporting data from 76 in vivo experiments. The experiments included in the database recorded methane production and rumen protozoa concentration measured on the same groups of animals. Quantitative data such as diet chemical composition, rumen fermentation and microbial parameters, and qualitative information such as methane mitigation strategies were also collected. In the database, 31% of the experiments reported a concomitant reduction of both protozoa concentration and methane emission (g/kg dry matter intake). Nearly all of these experiments tested lipids as methane mitigation strategies. By contrast, 21% of the experiments reported a variation in methane emission without changes in protozoa numbers, indicating that methanogenesis is also regulated by other mechanisms not involving protozoa.
Experiments that used chemical compounds as an antimethanogenic treatment belonged to this group. The relationship between methane emission and protozoa concentration was studied with a variancecovariance model, with experiment as a fixed effect. The experiments included in the analysis had a within-experiment variation of protozoa concentration higher than 5. 3 log 10 cells/ml corresponding to the average s. e. m. of the database for this variable. To detect potential interfering factors for the relationship, the influence of several qualitative and quantitative secondary factors was tested. This meta-analysis showed a significant linear relationship between methane emission and protozoa concentration: methane (g/kg dry matter intake) = -30. 7 + 8. 14 × protozoa (log 10 cells/ ml) with 28 experiments (91 treatments), residual mean square error = 1.
Selection of publications We included in the database only publications reporting in vivo data of both CH 4 emission and rumen protozoa concentration measured on the same groups of animals. To find publications, bibliographical databases of editorial platforms (Web of Knowledge, ScienceDirect and Google Scholar) were interrogated, with CH 4 , protozoa and ruminants as keywords. Unpublished experiments from our research group (INRA, UMR1213 Herbivores) were also added. Quantitative factors (intake, chemical composition of the diet, total tract digestibility, rumen VFA concentrations, rumen pH, rumen bacteria and methanogen concentrations, and rumen sampling time relative to feeding time) were added to the database when available, with s. e. and statistical differences between treatments. Reported data of rumen parameters in kinetics were averaged.
, 2004 ). An experiment was defined as one control treatment and at least one experimental treatment testing one or several CH 4 mitigation strategies with the same basal diet. When relevant, one publication could supply different experiments, if controls were different. The final curated database contained 59 papers (number of experiments N exp = 76, number of treatments N t = 219), including 6 unpublished experiments (N t = 24) from our research group. The list of published papers used is given in Supplementary Material S1.
Experiments were considered as reporting a significant variation in protozoa or CH 4 if the within-experiment variation of the parameter was, respectively, higher than one or two times the database average s. e. m. for the parameter. The threshold levels for protozoa and CH 4 were then 5. 3 log 10 cells/ml (2. 2 × 10 5 /ml) and 1. 1 g/kg DMI, respectively.