dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2005). This was accommodated with the nlme R package  (Pinheiro et al. , 2018)  and validated using AIC scores combined with residual plots for models developed using restricted maximum likelihood. The effect of Treatment was considered as significant when the null vs main effect models (using maximum likelihood estimation) differed significantly (ANOVA, p < 0. 05). To summarize the environmental conditions recorded at the four sites in the three periods and to illustrate potential differences between Farm and Reference treatments, a principal component analysis (PCA) was performed using representative benthic (sediment organic carbon, nitrogen and phosphorus contents, fauna and macrophyte biomasses) and water column (nutrient, chlorophyll, pheopigment concentrations and ratios) parameters.
Acknowledgements This work is a contribution to the  MORTAFLUX program , funded by  Ifremer Scientific Direction  and the  EC2CO BIOHEFECT action  (Coordinator:  M. Richard ). The Postdoctoral fellowship of É. Lacoste was funded by  Ifremer  and the  University of French Polynesia. Macrofauna analysis was funded by  UMR MARBEC. Many thanks to  F. Tarbouriech , head of the  MEDITHAU  farm for allowing us to take samples from his tables. Thanks also to his employees,  L. Ritter ,  P. Avila  and  PE. Galavielle  for providing information on table densities and for help during oyster sampling. We thank  S. Poirier ,  E. Hatey  and  C. Hubert  for lab analyses,  N. Cimiterra  for the map realization (Fig. 1) and  C. Pelaprat  for macrofauna identification. The authors are very grateful to their retired colleagues:  S.
8 for sulfates, between 1 and 5 for carboxylic acids, and between 8. 5 and 12. 5 for amines  (Stumm and Morgan, 1996; Braissant et al. , 2007). Using Protofit 2. 1 software  (Turner and Fein, 2006) , and assuming three non-electrostatic binding sites, the pKa of the proton-binding sites as well as their densities could be estimated (Figure  6 ). A first pKa (pK1) between 1. 9 and 1. 98 was attributed to either carboxyl or sulfate groups and accounted, respectively, for 73 and 72% of site density in the surface and 6. 08 m EPSs. The second buffering zone (pK2) was located at 3. 4 and represented 23% of site density in surface EPSs. In 6. 08 m EPSs, pK2 was found at 5. 32, accounting only for 5% of site density. The third buffering zone (pK3) located at 9. 28 (surface EPSs) and 10. 53 (6. 08 m EPSs) was attributed to amino groups, with a higher site density at 6.
Relationships between EPSs and sediment grain size Estuarine microphytobenthic biofilms grow in the intertidal zone of estuaries, preferentially on muddy and stable substrates (e. g. ,  Underwood and Paterson, 2003; Orvain et al. , 2012). However, EPS components show a differential response to sediment grain size. In surface sediments or short sedimentary cores (<30 cm), the amount of colloidal carbohydrates is often positively correlated with the proportion of mud (i. e. , particles  Acid-base titration curves from exopolymeric substances (EPSs) extracted from the surface biofilm and the bottom of the BXN Long Core (depth = 6. 08 m). The titration curves obtained were analyzed using PROTOFIT 2. 1 software, and both show three buffering zones. The first zone located around pH 2 is attributed to carboxyl or sulfate groups, the second zone located near pH 5 is attributed to carboxyl groups, and the third buffering zone near pH 9.
2001). Another obstacle was how to draw conclusions from various pieces of data and information. 1994 , 2016; WMO 2009). Quantifying relatively small long-term trends in CO2 and other GHG concentration and fluxes against a background of much larger short-term variations caused by the 'natural' carbon cycle requires highly precise and accurate observations. To decrease The variables and related costs for all ICOS observations are detailed in the ICOS Handbook, released every 2 years (ICOS 2020a). For example, to build one fully equipped atmosphere or ecosystem station (Class 1) costs between 0. 5 and 1 M€ (not including personnel costs), whereas costs for stations having only a subset of observations (Class 2) can be between 0. 1 and 0. 3 M€. Maintenance requirements and collection of ancillary data are also provided, with the most significant component being person-power, ranging from 0.
Quality Assurance and Control (QA/QC) Within the CarboEurope IP project  (Schulze et al. 2010 ) the challenges to achieve highly compatible atmospheric data became obvious. Large efforts were undertaken to assess the compatibility of atmospheric measurements done by different laboratories at different observational sites. Yet, results from these exercises repeatedly yielded evidence that the WMO compatibility goals were not met by all participants and for all tracers. Biases between laboratories could sometimes be of the same order of magnitude as the atmospheric signals that should be captured and it was not possible to define a network data compatibility. This was motivating the ICOS concept with highly standardized measurement approaches at the observatories (including aspects such as instrumentation, procedures to account for atmospheric humidity, and calibration procedures) and the establishment of central facilities that assess the adequate performance of all installed analyzers and assure transparent data processing (ATC), as well as the consistency of sample measurement results and reference gas assignments that are used within the monitoring network (CAL).
To have the ability to make a defendable uncertainty assessment that is required for observational data (WMO 2020b) the following QA/QC approaches are applied that cover all levels of the observational Both optimal sets of variables and practical feasibility were considered when harmonizing the observations, which resulted in a compromise suitable for high quality and long-term continental scale observation system  (Franz et al. 2018)  To guarantee the quality of observations, in all three network components (atmosphere, ecosystem, and ocean), it is necessary for a station to pass an ICOS station certification process. Here, the station characteristics are evaluated, its compliance with measurement protocols and standards is analyzed, and data transfer and quality are evaluated by the respective Thematic Centres during a test measurement period of a few months.
2010) , or on urban systems. Mitigation capacity of urban areas as well as their adaptation capacity will need much deeper attention as the urban population is continuously growing and urban areas represent the major sources of GHGs in Europe and in most of the continents  (Calfapietra et al. 2015). thanks ICOS data is widely used in publications from various scientific fields. The amount of ICOSrelated publications per year have increased from 30 in 2012 to roughly 200 in 2020, and the citations from 600 to 11 000, respectively (https://www. icos-cp. eu/science-andimpact/society-impact/references). The publications are associated to almost 60 categories with the two largest being meteorology and atmospheric sciences (37% of all publications) and environmental sciences (34%) (ICOS 2021). The cross-domain integration in ICOS allows us to comprehensively address the biogeochemical fluxes of carbon and GHGs and to identify and study existing gaps in knowledge.
A recent example are the 17 publications, based on data from more than 100 stations, following the drought in Europe in 2018. The drought was analyzed from how it was detectable in the atmospheric station network and how it affected ecosystem processes and GHG budgets, to regional assessments of its influences on ecosystem carbon exchange, and relations to major crops  (Peters et al. 2020). ICOS made this rapid scientific response possible by building the foundation for fast action, by harmonizing observations and centralized data processing, by analyzing the data in near-real time to detect anomalies in drivers and ecosystem responses, by facilitating networking of scientists, and by providing virtual solutions for joint work. The results show that the drought affected more the productivity of crops and grasslands than forests, which protected themselves by reducing their evaporation and growth, leading to decreased uptake of carbon dioxide  (Peters et al.
to the PI and Central Facilities work that agreed on a continuous data submission and Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10. 1175/BAMS-D-19-0364. 1. Unauthenticated | Downloaded 11/02/21 08:25 AM UTC adoption of an open data license (Creative Commons Attribution 4. 0 International, which also allows commercial use). Additionally, the services provided to make data distribution easier and assignment of Digital Object Identifier (doi) to datasets are major advances to improve and promote open data. To serve various user needs, different levels of the data are openly accessible with different level of processing and quality check. Much attention has been paid to the metadata that follow the specifications defined by the Carbon Portal in collaboration with the Thematic Centres, also considering existing international standards.
All steps of data flows were designed based on the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. , 2016), giving the user sufficient tools to interpret the data (ICOS 2015). Different levels of data are stored throughout the process, from raw sensor data (Level 0), to the automatically calibrated near-real-time data (Level 1; available within 24 hours of the measurement) to the final, quality-checked data (Level 2). All the data are passed on to the Carbon Portal, which provides free and open access to ICOS data. The data are minted with Persistent Identifiers to provide unique identification and citation of the datasets and their contributors (ICOS 2019). The Carbon Portal offers tailor-made tools and services (Fig. 3) and distributes products (Level 3) that are created by the scientific community based on ICOS data and possibly from other data sources (Fig.
Acknowledgements Academy of Finland  (Grants nr  281255 ,  319871 ,  320124 ,  329221 ). , the  Danish Agency for Science and Higher Education ,  Danish Ministry of Energy, Utilities and Climate ,  EU  (Grants nr 211574, 730944),  Finnish Ministry of Transport and Communication, Flemish Fund for Scientific Research  ( FWO Grant nr   G0H3317 ),  French Ministry of Research (MESRI) ,  German Ministry of Education and Research (BMBF) ,  German Ministry of Transport and Digital Infrastructure (BMVI) , the  Italian Ministry of Universities and Research, The Netherlands Ministry of Education, Culture and Science , the  Netherlands Organisation for Scientific Research. , the  Ministry of Education, Youth and Sports of the Czech Republic , The  Natural Environment Research Council of the UK, Norwegian Environmental Agency ,  Norwegian Ministry of Climate and Environment, Research Council of Norway, Swedish Research Council  (Grant nr  2019-00205 ),  Swiss National Science Foundation  ( ICOS-CH Phase 1  and Phase 2,  20FI21_148992  and  20FI20_173691 ) and the  ETH domain.
2020). flows, covering atmosphere and ecosystem sciences (Schulze et al. 2009). In parallel, the ecosystem, and ocean stations was addressed by centralizing the quality control and data CarboOcean IP conducted over 2005-2009 developed systematic ocean carbon observations processing in three respective Thematic Centres with specific experience and knowledge. To and analysis across Europe. The observations collected in the context of these projects were allow measurements of required precision, the Central Analytical Laboratories was designed an important example to demonstrate how a large and coordinated network could provide a to provide calibration gases to atmospheric and ocean monitoring stations. The process for unique dataset valuable for the modeling activities to estimate continental scale GHG fluxes scientific development was planned on the interactions between these components and the (e.
One concern with using J o u r n a l P r e -p r o o f Rose Bengal is that it may stain the protoplasm of dead foraminifera that may be relatively well-preserved for long time periods under the generally anoxic conditions prevailing in deep sediments  (Corliss & Emerson, 1990; Bernhard, 2000). We therefore applied very strict staining criteria (i. e. , all chambers, except the last chamber, stained in bright pink), and compared doubtful individuals to perfectly stained specimens of the same species found in the superficial sediment layers. For miliolids, doubtful specimens were broken to inspect test interior. Most stained foraminifera were identified at species level and checked with the World Foraminifera Database  (Hayward et al, 2021)  for current taxonomic nomenclature (see Appendix A for taxonomical references and Appendix B for census data).
J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f ) ( ) , ( ) , ( ) ( ) , ( ) , ( ) , ( ) , ( ) , ( 100         Nstations Nstations B ls Nindividua ls Nindividua A B A IV i i i i i i i i      Nindividuals(i,α) is the mean number of individuals belonging to the species i and present in the cluster α. Nindividuals(i) is the sum of the mean numbers of individuals of species i occurring in all clusters. Nstations(i,α) is the number of stations in the cluster α where the species i is present. Nstations(α) is the total number of sites in the cluster α. P values were calculated based on 999 permutations using R package "labdsv" (Roberts 2019; R core team 2020).
project (European Union's Horizon 2020 Research and Innovation program under grant agreement no. 654410), (2) the VOG project (LEFE-CYBER and EC2CO-PNEC), and (3) the MAGMA project (COTE cluster of Excellence ANR-10-LABX-45). NASA is acknowledged as a source of MODIS Satellite images (https://modis. gsfc. nasa. gov/). Finally, we thank both reviewers who have provided very useful comments to improve the overall quality of this paper.
Here, we leveraged contemporary advances in Earth Observation-cloud computing, open satellite data, and machine learning-with field observations through a cloud-native geoprocessing framework to account the spatially explicit ecosystem extent of P. oceanica seagrass across its full bioregional scale. Employing 279,186 Sentinel-2 satellite images between 2015 and 2019, and a human-labeled training dataset of 62,928 pixels, we mapped 19,020 km 2 of P. oceanica meadows up to 25 m of depth in 22 Mediterranean countries, across a total seabed area of 56,783 km 2. Using 2,480 independent, field-based points, we observe an overall accuracy of 72%. We include and discuss global and region-specific seagrass blue carbon stocks using our bioregional seagrass extent estimate. As reference data collections, remote sensing technology and biophysical modelling improve and coalesce, such spatial ecosystem extent accounts could further support physical and monetary accounting of seagrass condition and ecosystem services, like blue carbon and coastal biodiversity.
, 2018)  using the cloud geospatial platform of the Google Earth Engine (GEE)  (Gorelick et al. , 2017). We utilized this algorithmic framework to map, for the first time, the P. oceanica seagrass ecosystem extent at its whole bioregion scale of 56,783 km 2 that includes 22 countries. The GEE cloud geospatial platform enabled the storage, processing, and analysis of a mosaic of 279,186 open satellite Sentinel-2 images  (Gascon et al. , 2017)  between 2015 and 2019, the mosaic classification via the cloud-based machine learning (ML) algorithm of the Random Forests (RF)  (Gislason et al. , 2006) , and the development of an inventory of human-labeled training data to guide the ML-based classification. For the latter, we annotated 1,748 training blocks consisting of 62,928 100-m 2 pixels indicative of the different Mediterranean benthic classes i.
The Cloud-Native Earth Observation Framework As part of this study, we improved a recent cloud-native algorithmic framework  (Traganos et al. , 2018). This framework was built upon the advances in Earth Observation technology in terms of open petabyte-scale satellite datasets, high-performance cloud computational power, supervised machine learning, webbased visualization capabilities, and human-guided design of large-scale training data suitable for the supervision of the ML component-all of which are powered by the cloud infrastructure of the GEE. The cloud-native existence of the evolved Earth Observation framework (Figure  1 ) allowed a time-and cost-efficient scalability in three dimensions: space (e. g. , region, country, basin), time (e. g. , monthly, seasonal, annual, multiannual), and satellite data input.
The Multi-Temporal Sentinel-2 Satellite Image Mosaic The aforementioned three components were adjusted according to our mapping task. To map the basin-scale seagrass distribution of P. oceanica, we scaled up the framework  (Traganos et al. , 2018)  throughout the first 25 m of the depth of the Mediterranean, a total of 56,783 km 2. We identified this depth range utilizing the EMODnet Bathymetry Digital Terrain Model (DTM) for the European Seas of 2018 (https://www. emodnet. eu/newhigh-resolution-digital-terrain-model), which is a composite of bathymetric datasets from different sources at ~115m x 115 m resolution. We empirically selected the 25 m cut off depth as the most representative deep limit of seabed detection of the Sentinel-2 satellite-guided by water quality and human expert knowledge-in both the western and eastern basin part of the Mediterranean  (Poursanidis et al.
Training Data Before describing the artificial intelligence component in the heart of our cloud-native framework, it is important to describe our training data design-essentially, the type of data that guides the AI. The training data were labeled following human-guided photointerpretation of the Sentinel-2 image mosaic by an expert on both Earth Observation image analysis and the nature of the Mediterranean coastal seascape. The expert spent 100 working hours within the cloud geospatial environment to annotate polygons indicative of the presence of three habitat classes: a) seagrasses (P. oceanica), b) optically deep water, and c) rocky and sandy seabed (Table  1 ). We decided to design polygons on P. oceanica seagrass meadows and not on other Mediterranean seagrass species (e. g. , C. nodosa, Z.
marina) as the sparse natural distribution of the latter species would have caused confusion on the 10 m spatial resolution of Sentinel-2. Therefore, it is expected that the spectral similarities between P. oceanica and other seagrass beds may have caused over-estimations and misclassification of the former class in our mapping effort. The human photointerpreter designed the training data by choosing polygons from all depths (shallow to deep), distribution (eastern and western basin), and density gradient (sparse to dense) of the studied habitats. Sequentially, the final training dataset consisted of 1,748 polygons which were first reduced to centroids (Figure  2 ) and then were grown to blocks of a 30-m circular radius buffer around the centroids i. e. , ~36 Sentinel-2-pixel blocks of 3,600 m 2 each (Table  1 ).
Validation Data and Accuracy Assessment To assess the accuracy of our bioregional map, we synthesized existing independent field-based validation data of P. oceanica seagrass meadows and neighboring habitats. During this synthesis, we collected high-resolution seagrass habitat maps developed previously in the Mediterranean by trained experts and scientists in the seagrass domain resulting in in situ data of total coverage of 3,274 km 2. Table  2  indicates information on the coverage, temporal range, and source of the reference data. In addition to approaching our annotated data with as much independence as possible to reduce possible bias, the reference data featured higher quality and finer spatial resolution  (Finegold et al. , 2016)  following field collection by trained experts via snorkeling, diving, and/or with the use of other technical equipment.
DISCUSSION Using a cloud-native Earth Observation framework for big-satellite-data processing and analysis, we showcase here the first data-driven, spatially explicit seagrass extent accounting across entire bioregional scale. Featuring a plethora of cutting-edge frameworks and algorithms in Earth Observation such as multi-temporal data analytics, machine learning, cloud computing, and big-data processing, we map 19,020 km 2 of area of the seagrass P. oceanica across the entire basin using 10 m 279,186 open Sentinel-2 satellite image tiles (2015-2019) and 65,408 reference data points. Undoubtedly, the most important novelty of our mapping effort is the fact that we designed, synthesized, and employed a single and consistent data source-the multi-temporal Sentinel-2 data mosaic-within the moderately small temporal window of 4 years to account the bioregion-wide seagrass ecosystem extent.
This was also highlighted in a recent national-scale seagrass mapping venture in the Greek territorial waters  (Traganos et al. , 2018)  which yielded four times more seagrass habitats than the inventory of UNEP-WCMC and Short (2018). Last but not least, we calculated only one-sixth of the modeledbased area of Jayathilake and Costello (2018) (118,913 km 2) , which nonetheless was not an actual seagrass area estimate, but rather the potential regional occurrence of seagrasses in the basin according to their habitat suitability. Such modelguided output could complement the ability of the two former data syntheses to guide the scalability of data-driven mapping approaches like the one presented here. It is also worth noting that our basin-wide mapping estimate features a producer's accuracy of 55% which increases the uncertainty in the aforementioned comparisons.
ACKNOWLEDGMENTS In France, data used to build the 1:10000 marine habitat map were collected by  Andromède Océanologie ,  Agence de l'Eau RMC, Conservatoire du Littoral, DREAL PACA ;  Egis Eau ,  ERAMM, GIS Posidonie, IFREMER, Institut océanographique Paul Ricard, Nice Côte d' Azur, TPM, Programme CARTHAM-Agence des Aires Marines Protégées, ASCONIT Consultants, COMEX-SA, EVEMAR, IN VIVO, Sentinelle, Stareso, Programme MEDBENTH, Université de Corse (EQEL) ,  Ville de St Cyr-sur-mer ,  Ville de Cannes ,  Ville de Marseille ,  Ville de St Raphaël  and  Ville de St Tropez. We acknowledge and appreciate the assistance of an anonymous proof-reader towards improving the quality of this manuscript.
Acknowledgements The paper is dedicated to the memory of  Bernard Pittet , former supervisor of F. S. and R. V. who passed away prior to the writing of this manuscript and who helped us improve our understanding of sedimentary processes associated with exceptionally-preserved fossils in the Fezouata  Shale. Jan Ove R. Ebbestad  is thanked for providing mollusc identifications and photographs. Mohammed " Ou Said" Ben Moula  and his family are acknowledged for their key involvement in fieldwork and collection of the specimens that are now housed at the  Museum of Comparative Zoology at Harvard University. Joseph Botting ,  Peter Van Roy ,  Christian Klug , and  Marika Polechová  are thanked for providing comments that helped us improve this work. This paper is supported by Grant no. 2020M683388  from the  Chinese Postdoctoral Science Foundation  awarded to F.