dataesr/scientific-paragraphs-categorization huggingface dataset:
The authors thank  Jean-Paul Vernier  for helpful discussions and  David Huber  for technical support. Financial support. This research has been supported by the  Agence Nationale de La Recherche  (grant no. ANR-17-CE01-0015 ,  TTL-Xing ) and the  VOLTAIRE LabEx (VOLatils -Terre ,  Atmosphère et Interactions -Ressources et Environnement ) as part of  WP7  (convention no. ANR-10-LABX-100-01 ). Corinna Kloss  was partly funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -409585735. Felix Ploeger  was funded by the  Helmholtz Association  under grant  VH-NG-1128  ( Helmholtz Young Investigators Group ASPECi ). Silvia Bucci  was supported by  EU FP7  (grant nos. 603557 ,  CEFIPRA5607-1 ). IASI-related activities were supported by  CNES  ( TOSCA IASI  project). Review statement.
The Teledyne Optech Titan dual wave LiDAR managed by Nantes Rennes university platform can record a FWF on one selected channel at a time. It was installed to record the 532-nm signal for bathymetric purposes. The airborne campaign was conducted at an altitude of 400 m with the The Teledyne Optech Titan dual wave LiDAR managed by Nantes Rennes university platform can record a FWF on one selected channel at a time. It was installed to record the 532-nm signal for bathymetric purposes. The airborne campaign was conducted at an altitude of 400 m with the cooperation of GEOFIT and PIXAIR companies over two days, 6-7 October 2017. The scan angle of the rotating mirror was ±14 • giving a field of view (FOV) of 28 • and other details of data acquisition are given in Appendix A. A 1 m × 1 m per pixel Digital Surface Model (DSM) of the first 1064 nm returns envelope was generated for a quick overview showing tide variation between swath and days (Figure  1a , see also high resolution figures in Supplementary Materials).
All the bathymetric data were processed by  [37]  with Caraïbes software  [38]  from  [39]. Two profiles were acquired from 5 m to 4. 5 m of water depths, in the east of the Bay, perpendicular to the beach, in front of the harbor. These two profiles gave calibrated measurements both in bathymetry and position. Thus, they may be used as calibration data for further acquisition. The seismic data confirm that the sea bottom is covered by sandy formations.
Discussion The method presented in this work uses a straight ray tracing procedure to build a mosaic of FWF on a 1-m-wide pixel grid with a vertical range resolution of 0. 15 m along a laser pulse light path with preservation of incident angle information in each pixel. Because of the narrow scanning angle used, the light path in water was successfully corrected along a 0. 15 m range direction in the initial FWF data cube geometry without translation between pixels. This step could have been improved by considering the exact orientation of the water interface with air. However, the strong hot spot effects observed in water and most wet areas, such as sediment ripple marks exposed at low tide, suggest that capillary waves  [40]  can play an important role as shown also by  [21]. A full characterization would therefore require a lot more work and a much higher resolution note compatible with the monitoring of hundreds of shoreline kilometers.
At a scale of 1-m-wide pixel for a bathymetry down to 5-m depth NGF, the fast procedure used seems appropriate. A 3-m wide pixel for 10 m depth bathymetry is also possible for large shoreline monitoring. Above that level marine applications are more time efficient. La Baule bay data was acquired on two different days because of temporary technical problems in 2017. A new campaign in 2018 covering 16. 7 km 2 was done in a single day in less than 3 h. As for  [22]  all figures presented in this work use nearest pixel sampling without pixel aggregation and all calculations were done at the 1 m pixel resolution to prevent any FWF stretching on the slope. The pixels in Figure  1  are 1 m square, but only 1 out of 100 are shown. Aggregation of pixels are only possible on final maps after range and echo extraction.
Acknowledgments: We would like to thank  William Gentile ,  Cyril Michon  and  Emmanuel Gouraud  from  GEOFIT Company. Jean-Jérôme Houdaille from PIXAIR Company ,  Anca Dobrinescu ,  Paul La Rocque ,  Mark Topping  and  Michel Stanier , from  Teledyne Optech Company. Martin Juigner  from the  LETG Nantes  for GPS ground control points and  Martin Sanchez  from the  LPG Nantes for turbidity data in La Baule. Particular thanks to  Dimitri Lague  from  Geosciences Rennes  and  Erwan Le Menn  from the  LPG Nantes , for their respective management and contribution to the Nantes Rennes LiDAR platform and  Laurence Huber Moy  from the LETG in Rennes for her help in creating of the shared platform between Nantes and Rennes. We would also like to thank the reviewers who helped us to better focus this publication on methodology and main results.
Models and Experiments The CCMI Hindcast experiment consisted of both free-running REF-C1 (FR) and specified-dynamics REF-C1SD (SD) simulations, both of which were constrained with observed SSTs and SICs. Here we report the details of how the SD simulations were implemented among models, based on feedback we received in response to a survey that was distributed among CCMI model contact leads. Among those simulations we only show results from output that was uploaded to the British Atmospheric Data Centre (BADC) archive (ftp://ftp. ceda. ac. uk) and/or provided to us via personal communication. Output from the WACCM and CAM simulations was obtained from the NCAR Earth System Grid portal (https://www. earthsystemgrid. org/). Table  1  lists the modeling groups that responded to our survey, what type of model was used for the SD simulation (offline CTM or online nudged CCM), and which source of meteorological fields was used.
Sources of Convective Mass Fluxes In addition to differences in the resolved flow among the simulations, another large source of differences are the (parameterized) convective mass fluxes used to simulate convective transport. These were either taken from the same analysis dataset from which the large-scale flow fields were obtained (e. g. the NASA GMI-CTM) or recalculated online using the model's own convective parameterization. The latter approach was used mainly in the nudged simulations, although some offline models also recomputed the convective mass fluxes. A broad range of convective parameterizations are used including relaxed and/or triggered schemes as described in  Moorthi and Suarez (1992)  and  Zhang and McFarlane (1995)  (e. g. , CESM1 CAM4-chem and WACCM, CMAM, GMI-CTM), nearly instantaneous adjustment schemes along the lines of  Arakawa and Schubert (1974)  (e.
Most of the REF-C1SD simulations use the Hadley Centre Ice and Sea Surface Temperature (HADISST) dataset (Rayner et al. (2003)), as recommended. Some simulations, however, were forced with climatological SSTs and SICs taken from other datasets derived both from models (e. g. TOMCAT CTM) and other observational sets. The latter include simulations constrained with monthly mean SSTs as used in the AMIP simulations and described in Hurrell et al. (2008) (e. g. IPSL LMDz-REPROBUS). The former include simulations that were constrained with SSTs from Reynolds et al. (2000) (e. g. the NASA GMI-CTM simulation) and ERA-Interim (e. g. EMAC).
Reference: Chipperfield et al. (2006) A6 IPSL LMDz-REPROBUS The IPSL LMDz-REPROBUS REF-C1SD nudged simulation is constrained with ERA-I six-hourly instantaneous fields and forced with SSTs and SICs from the AMIP II dataset. The nudged fields U, V and T are linearly interpolated spatially to the model grid and linear interpolated temporally to the model timestep (1/2 hour). Nudging occurs everywhere and operates on a timescale ranging up to 3 hours, depending on altitude. Neither spectral nudging nor a pressure correction for surface pressure drifts is used. Convective mass fluxes are calculated online using the convection scheme of Dufresnes et al. (2013). References: Marchand et al. (2012), Szopa et al. (2013) and Dufresne et al. (2013) A7 CMAM-SD The CMAM REF-C1SD simulation is nudged to ERA-I six-hourly instantaneous fields and forced with HadISST1 boundary conditions.
Acknowledgements. We thank the  Centre for Environmental Data Analysis (CEDA)  for hosting the CCMI data archive. We acknowledge the modeling groups for making their simulations available for this analysis, and the joint  WCRP SPARC/IGAC Chemistry-Climate Model Initiative  ( CCMI ) for organizing and coordinating this model data analysis activity. In addition, C. O. wants to thank the high-performance computing resources provided by the  NASA Center for Climate Simulation (NCCS ) as well as support from the  NASA   Modeling, Analysis and Prediction (MAP) program. D. W. acknowledges support from  NSF  grant  AGS-1403676  and  NASA  grant  NNX14AP58G. The EMAC simulations have been performed at the  German Climate Computing Centre (DKRZ)  through support from the  Bundesministerium für Bildung und Forschung (BMBF).
Data availability. The majority of the data of CCMI Phase 1 used in this study can be obtained through the British Atmospheric Data Centre (BADC) archive (ftp://ftp. ceda. ac. uk). For instructions for access to this archive see http://blogs. reading. ac. uk/ccmi/badc-data-access. Output from the WACCM and CAM simulations was obtained from the NCAR Earth System Grid portal (https://www. earthsystemgrid. org/). In addition to the CCMI and NCAR data we obtained MERRA U, V, T from the assimilated (ASM) (not analysis) collections, located on.
Image processing The intensity of the second harmonic signal of collagen fiber as a function of polarization angle of the incident laser beam can be written as: 𝐼𝐼 𝑆𝑆𝑆𝑆𝑆𝑆 = 𝑐𝑐. ��(sin(𝜃𝜃 𝑒𝑒 -𝜃𝜃 0 )) 2 + � 𝜒𝜒 𝑧𝑧𝑧𝑧𝑧𝑧 𝜒𝜒 𝑧𝑧𝑧𝑧𝑧𝑧 � (cos(𝜃𝜃 𝑒𝑒 -𝜃𝜃 0 )) 2 � 2 + � 𝜒𝜒 𝑧𝑧𝑧𝑧𝑧𝑧 𝜒𝜒 𝑧𝑧𝑧𝑧𝑧𝑧 � 2 (sin(2(𝜃𝜃 𝑒𝑒 -𝜃𝜃 0 ))) 2 � Equation 1 where � 𝝌𝝌 𝒛𝒛𝒛𝒛𝒛𝒛 𝝌𝝌 𝒛𝒛𝒛𝒛𝒛𝒛 � and � 𝝌𝝌 𝒛𝒛𝒛𝒛𝒛𝒛 𝝌𝝌 𝒛𝒛𝒛𝒛𝒛𝒛 � are second-order susceptibility tensor element ratios; θe and θ0 are the incident polarization angle and collagen fiber angle, respectively; and c is a normalization constant. ImageJ (NIH, Bethesda, MD) was used to analyze the images which were binned to obtain regions of interest (ROIs) of 2 × 2 pixels. Collagen orientation angles were evaluated for each ROI by fitting the binned images to a Levenberg-Marquardt algorithm using Matlab (MathWorks, Natick, MA).
| Literature search We searched the literature from three different sources of information: grey literature, reference books and online consultation of databases and catalogs. We retrieved published material from four online search engines: ( i ) Google scholar for its flexibility and ability to recover reports and grey literature; ( ii ) the online database of the Congo Basin Forest Partnership (CBFP: http://www. pfbc-cbfp. org/actualites/items/EDF_2010-F. html ). This latter site has provided references based on news (recent and past) from the Congo Basin countries in Central Africa; ( iii ) the use of the APES online database "Ape Populations, Environments, and Surveys" and "IUCN-New Population, Environments and Surveys Database" ( http://apes. eva. mpg. de/ and http://www/iucn/org/en/news_homepage/news_par_date/3246/New-ape-population-e nvironments-and-surveys databases).
This site has provided, apart from the results on primates, several reports of inventories of large mammals made in the forest; and finally, ( iv ) the use of international publication databases including Ingenta Connect, Wiley InterScience, SpringerLink and Unicat. We queried all databases and catalogs with the following combination of keywords: "census" "methods", "monitoring", "survey", "Congo basin", "mammal", "herbivorous mammals", "ungulates", "fauna", "density-ies", "duiker" back to 1970.
| RESULTS We recovered 31 publications that addressed the question of duiker abundance estimation methods. We identified seven combinations of sampling designs and methods to estimate duiker abundance: (  1  and (8) tracks count. These methods can be divided into two broad categories (Fig. 1 ) according to  Eberhardt (1978) : direct sampling vs. indirect sampling depending on whether individuals are mobile and detected with certainty, or not. We briefly describe below these different methods. The distance sampling  (Buckland et al. , 2001)  used in methods 4 to 6 consists in walking parallel tracks in the forest in the same direction, then collecting data along these transects. The observations collected are the distance perpendicular to the transect of each seen duiker. Distance sampling is the only direct sampling methods suitable for moving animals  (Eberhardt, 1978)  and has been applied to duikers using two different sampling designs: ( i ) diurnal distance sampling method (DSM) when the observations are direct and take place during the day, the morning between dawn and 11:00 am or between 03:00 pm and 06:00 pm (Jost Robinson, Zollner, & Kpanou, 2016;  Koster et al.
ACKNOWLEDGEMENTS We thank the  Government of the Republic of France  through the  Campus France program , the  Government of the Democratic Republic of Congo (DRC)  through the  Ministry of Environment and Sustainable Development and The Ministry of Higher Education and University, the Program for the Conservation of Forest Ecosystems in Central Africa (PACEBCo)  of the  Central African Forests Commission (COMIFAC)  and the  Nature Plus of Belgium , for their financial, technical, logistical and didactic support. Our thanks also go to  Punga Kumanenge Julien  and  Palata Jean-Claude  for.
METHODS A literature review was carried out to know the state of knowledge about regurgitation and spitting of seeds during rumination, without considering a priori if the articles refer specifically to seed dispersal. We consulted reference databases such as Science Citation Index, Science Direct, Scopus, and Google Scholar, among others. Search terms included several combinations of "seeds, " "seed dispersal, " "endozoochory, " "rumination, " "spitting, " "regurgitation, " "ungulates, " "ruminants, " "livestock, " "cattle, " "goat, " "sheep, " "deer, " and "antelope. " We also searched for articles citing  Feer (1995)  and  Prasad et al. (2006)  and examined all references contained in each of the previously selected articles mentioning spitting of seeds during rumination. More than 1000 papers were considered, but only 40 (plus two communications in litt.
departments. bucknell. edu/biology/resources/msw3/) for mammals. Seed mass was obtained from the original papers on seed dispersal, when available, or from the Seed Information Database of the Royal Botanic Gardens, Kew (http://data. kew. org/sid/). For ruminants' body weight and other characteristics we used  Bodmer (1990)  and the Handbook of the Mammals of the World, vol. 2  (Wilson and Mittermeier, 2011 ; Table  2 ).
FUNDING JF was funded by the  Portuguese FCT  (grant  IF/00728/2013 ). ACKNOWLEDGMENTS Many herders in Morocco and Spain helped us to obtain information from their animals. F. Cuzin  and  F. Hiraldo  offered us unpublished data on gazelles and cattle spitting seeds from the cud. G. Calvo  and  M. Jácome  helped us in fieldwork and  S. Conradi  in the laboratory work. S. Sridhara  and  C. Baltzinger  significantly improved a previous version of the manuscript.
Operational oceanography can be described as the provision of routine oceanographic information needed for decision-making purposes. It is dependent upon sustained research and development through the end-to-end framework of an operational service, from observation collection to delivery mechanisms. The core components of operational oceanographic systems are a multi-platform observation network, a data management system, a data assimilative prediction system, and a dissemination/accessibility system. These are interdependent, necessitating communication and exchange between them, and together provide the mechanism through which a clear picture of ocean conditions, in the past, present, and future, can be seen. Ocean observations play a critical role in all aspects of operational oceanography, not only for assimilation but as part of the research cycle, and.
, 2007) , and the start of the Argo Profiling Program in 2000  (Freeland et al. , 2010). Furthermore, since 1991, the Global Ocean Observing System (GOOS) 1 has coordinated efforts to generate impactful datasets. In this paper, we use the term operational oceanography to mean the activity of systematic and long-term routine measurements of the seas and oceans and atmosphere, and their rapid interpretation and dissemination (European Global Ocean Observing system EuroGOOS) 2. Operational oceanography systems are comprised of several major components: (1) observations, (2) data management, (3) ocean prediction systems (numerical models and data assimilation), and (4) a dissemination system to stakeholders (see Figure  1  adapted from  Schiller et al. , 2018). Operational oceanographic systems are information enhancing systems for ocean observations or, more simply, a value chain for ocean observations to meet societal needs  (Robinson and Lermusiaux, 2001).
High quality observations are therefore a requirement for the systems to be useful. 1 http://www. goosocean. org 2 http://eurogoos. eu In the late 1990s, the international Global Ocean Data Assimilation Experiment (GODAE) was launched to: (i) demonstrate the feasibility and utility of ocean monitoring and forecasting on the daily-to-weekly timescale and (ii) contribute to building a global operational oceanography infrastructure  (Smith and Lefebvre, 1997; Schiller et al. , 2018). Building on GODAE, GODAE OceanView was established in 2009  (Bell et al. , 2009)  to define, monitor and promote actions aimed at coordinating and integrating research associated with multi-scale and multidisciplinary ocean analysis and forecasting systems. In 2019, GODAE OceanView will become OceanPredict to continue expanding the activities of GODAE and GODAE OceanView, but with an added emphasis on ocean prediction as part of the broader network of international operational oceanography initiatives.
To achieve this goal, OceanPredict will develop close partnerships with entities such as the World Meteorological Organization (WMO), the United Nation's Intergovernmental Oceanographic Commission (IOC), and GOOS, as well as with organizations that focus on end user needs (e. g. ,  GEO Blue Planet). Operational oceanography begins with data from a variety of observational platforms that each provide a unique measure of ocean conditions. These include in situ measurements (e. g. , FIGURE 1 | A partition of components of operational oceanography. This figure is slightly modified from  Schiller et al. (2018)  to better represent the connectivity to Ocean Observations of all components and the flow of information to the end user. Orange boxes are the components related to end use of Ocean Observations and Predictions.
moorings, profiling floats, drifters, Conductivity-Temperature-Depth (CTD), tide gauges, etc. ) and remotely sensed observations (e. g. , satellites and high frequency radars). Ocean prediction systems combine the available curated observations with numerical models through data assimilation methods to estimate the state of the ocean. The ocean models can then be initialized from the analyzed ocean state and run forward in time to forecast ocean conditions hours, days, and even months (when coupled to an atmospheric model) into the future. By evaluating forecast and analysis system outputs against gridded  (Guinehut et al. , 2012)  and un-gridded observations, their accuracy and reliability can be estimated for specific user needs. Results can then be disseminated as data and visual products and made accessible to end users via the web.
The data assimilation techniques used in prediction systems provide valuable information; they can be used to quantify the forecast error as well assess the ability of specific observations to impact the analysis and forecast quality. This is valuable because it can highlight the importance of a particular observing system for a specific application. The operational oceanographic system itself can add value to observational data; outputs from prediction systems are gridded time-varying information that extends the utility of ocean observations far beyond the observation itself, enabling understanding of physical processes, even where no in situ observations have yet been made. This means that ocean prediction systems can provide oceanographic context to localized measurements (e. g. , a single mooring or ship transect), which under-sample an oceanic phenomenon (e.
g. , an oceanic eddy). This paper is organized as follows. In section Ocean Observations and Data Management, we describe major observation systems, their value to ocean prediction, and data management. In section Ocean Models, the numerical models used in the ocean prediction system are introduced and in section Ocean Data Assimilation, a brief overview of the data assimilation is described. Forecast verification is described in section Ocean Prediction Verification. Section Operational Oceanography Services and Stakeholders covers services and stakeholders and finally, in section Outlook and Recommendations, we provide a vision for improving operational oceanography by strengthening information exchange between observation and prediction groups.
Those having a surface structure can also provide atmospheric measurements. Coastal moorings are distributed almost along all continents, are usually maintained by national entities that also performs data quality control, and many of them contribute to the GOOS and their measurements are accessible following international standards. For the open ocean, the OceanSite program and the tropical mooring arrays: TAO/TRITON  (McPhaden et al. , 2010) , PIRATA  (Bourlès et al. , 2008 ), RAMA (McPhaden et al. , 2009)  are the reference data used by operational oceanography. These programs offer high quality data because moorings are replaced regularly, and instruments are calibrated, which implies a strong commitment by the community. Service cruises offer unique opportunities for additional multidisciplinary sampling of the ocean, repeated legs, launch of autonomous instruments, and testing of new sensors by qualified teams [for a complementary discussion, see  Foltz et al.
The high frequency radar network has grown worldwide rapidly in the past decade and is becoming an essential component of coastal ocean observation systems  (Rubio et al. , 2017). Accurate high frequency radar estimations can be used as benchmark for the rigorous skill assessment of operational wave and circulation models in key coastal areas  (Lorente et al. , 2016)  or can be optimally combined with ocean circulation models via data assimilation to enhance the model predictive skills. With regards to satellite remote sensing of the ocean, three new satellite observation systems have the potential to enhance the view of the ocean from space and provide new observations for ocean prediction improvement. The first, the Surface Waves Investigation and Monitoring (SWIM) instrument  (Hauser et al.