dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Satellite data from ocean colour sensors (e. g. , MERIS, Sentinel-3) can provide spatially coherent data for Chl-a. In coastal water, there is a high demand of reliable Chl-a images from end-users working for ecosystem management, fish and shell farming or eutrophication monitoring. However, because of the optical complexity of coastal waters as well as their high spatial and temporal variability, retrieving accurate Chl-a concentration remains challenging. In particular, if the study area is large or presents unknown or unpredictable water properties, the selection of the best Chl-a algorithm becomes very difficult. To help remote sensing users retrieving accurate Chl-a concentration, we presented QC tests for three popular Chl-a algorithms: OC4  (O'Reilly et al. , 1998) ; OC5  (Gohin et al.
, 2002)  and NIR-RED  (Gons, 1999). These QC tests are built from a newly developed methodology directly based on the relative difference between Chl-a in situ measurements and simultaneous MERIS water reflectance. The simplicity of these tests which only require water reflectance data and provides a binary mask (i. e. Chla estimation from this algorithm is reliable or not) makes them easily accessible to the remote sensing user community. In addition, the combined utilization of these QC tests with the switching methodology allows to ensure a good coverage with 83% of our dataset processed. Although QC tests were designed from MERIS data and would need further validation with OLCI data, a comparison of MERIS and OLCI-A applications has shown very consistent patterns suggesting that present QC tests are also suitable to OLCI data.
Acknowledgements This work has been funding by  European Maritime  and  Fisheries Funds through DG ENV/MSFD Second Cycle/2016 Program (EUNOSAT project  grant  11. 0661/2017/750678/SUB/ENV. C2 ) and by the  Swedish National Space Agency (Dnr. 175/17 ),  ESA   21524/08/I-OL ). It was also supported by funding from the  European Union 's  Horizon 2020 Research and Innovation Programme grant agreement N 810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation -PORTWIMS. We thank  ESA/EUMETSAT/EU Copernicus  for providing Sentinel-3 data and for permission to use them and  ESA  and  ACRI-ST  for developing ODESA and providing MEGS MERIS products (http://earth. eo. esa. int/odesa). ACRI-ST and ARGANS are acknowledged for the data processing and quality control on the MERMAID database.
1 ) -(the database is freely downloadable from https://doi. org/10. 1594/PANGAEA. 931493). The database includes the following categories of information: publication metadata (site name, author information, DOI or permanent link, journal title, publication year, main processes studied, GPS coordinates, country) sampling information (sampling year, core length, coring environment -e. g. , lacustrine, coastal, floodplain, corer type, corer diameter, subsampling increment, sediment compaction measured during the core collection) catchment and lake characteristics (name, size, main land use in the drainage catchment, slope, elevation range) description of the In this meta-analysis, both 137 Cs and 210 Pb xs activities and inventories were systematically decay-corrected from the sampling date to 1 January 2020 to allow comparisons between cores.
Publication characteristics Studies included in the database mainly provided dating of lacustrine sediment (47 %) and coastal-bay-estuarine sequences (29 %) and to a lesser extent, marine sediment (9 %), floodplain-riverine sediment (8 %), marsh-wetlandpeatland sediment (4. 5 %) and lagoon sediment (2. 5 %). These studies were published in a large variety of journals (n = 153) dealing mainly with geosciences, chemistry and environmental radioactivity. The most frequently encountered journals in the database are the Journal of Environmental Radioactivity (n = 49), Science of the Total Environment Studies compiled in the database mainly focus on contaminant-pollution-related topics (in 31 % of the publications) followed by investigations of sedimentation rates (26 %), monitoring of radionuclide concentrations in the environment (21 %), paleo-environmental reconstructions (9 %), and core chronology methods and limitations (3.
5 %; 241 Pu, 0. 3 %; 242 Pu, 0. 07 %; 243 Pu, 0. 07 %), potassium ( 40 K, 1. 5 %), radium ( 226 Ra, 0. 96 %; 228 Ra, 0. 14 %), strontium ( 90 Sr, 2. 8 %), technetium ( 99 Tc, 0. 07 %), thorium ( 228 Th, 0. 3 %; 232 Th, 0. 3 %; 234 Th, 0. 37 %) and uranium ( 236 U, 0. 07 %; 238 U, 0. 3 %). The 14 C was analyzed in 4. 5 % of the sediment cores. This isotope was exclusively analyzed by accelerator mass spectrometry (AMS) in the articles included in the database for providing long-term chronology.
We used the datasets from  GB West et al. (1997). These authors derived for the cardiorespiratory system many theoretical allometric scaling laws that are in good agreement with ecological observations. The morphological parameters used in our model are the trachea radius r 0 , the reduced trachea length l 0 , the generations number G of the bronchial tree, the generations number H of the acini and the amount ρ s of exchange surface area per unit of surface area of the alveolar duct wall. Each of these quantities follows an allometric scaling law that can be derived  The tree in blue mimics the acini, where oxygen is transported along the branches and also captured in the alveoli that cover the walls of the branches. The model of oxygen transport depends only on airways lengths and diameters but not on airways spatial distribution in the lungs.
Other exponents for metabolic rates, less pertinent for our study, have also been derived for specific subsets of mammals species, based for example on their size or on their athletic capacity  (Weibel, Bacigalupe, et al. , 2004; White and Seymour, 2003). The resolution of the model equations and the optimization process are performed using numerical simulations, as in  Noël and Mauroy (2019). The numerical strategy is described in Appendix 7 and details about the sensitivity of the model to its parameters are given in Appendix 8. The software and its details are available in the open data repository Zenodo  (Noël, Karamaoun, et al. , 2021).
Acknowledgements Version 6 of this preprint has been peer-reviewed and recommended by  Peer Community In Mathematical and Computational Biology  (https://doi. org/10. 24072/pci. mcb. 100005). We would like to thank  Dr. Elodie Vercken  ( INRAE, Institut Sophia Agrobiotech, France ) for fruitful discussions. This work has been supported by the  Agence Nationale de la Recherche , in the frame of the project  VirtualChest  ( ANR-16-CE19-0014 ) and of the  IDEX UCA JEDI  ( ANR-15-IDEX-01 ) and by the association  Vaincre La Mucoviscidose  ( RF20190502489 ).
Introduction Cancer accounts for a greater number of deaths among persons aged under 65 years than any other disease in the member states of the European Union (EU)  [1]. Cancer is a major public concern on the continent: almost onequarter of all global cancer cases occur in Europe, which is home to only one-tenth of the world's population  [2, 3]. In economic terms, cancer cost the EU almost V97 billion in 2018  [4]. Estimates of cancer burden at the national level are based on population-based cancer registry data and provide an important motivator for cancercontrol policies. In Europe, the individual members of the European Network of Cancer Registries (ENCR, http://www. encr. eu/) are the institutions hosting the national or regional cancer-registry information systems providing this baseline information.
European estimates of the cancer burden have been made available for over 30 years  [5e12]. The latest figures for 2020 are presented herein, and they are based on the collaboration between the European Commission's Joint Research Centre (JRC, which also serves as the ENCR secretariat), the Cancer Surveillance Branch of the International Agency for Research on Cancer (IARC, which also serves as the Secretariat of the International Association of Cancer Registries e IACR) with the support of the ENCR and IACR. Estimated incidence and mortality figures are based on several data sources, namely: the historical time series available in the European Cancer information System (ECIS) of the European Commission  [13]  and the data submitted to the project 'Cancer Incidence in Five Continents Vol. XI (CI5-XI)'  [14]  e for the incidence predictions; the World Health Organisation (WHO) database  [15]  e for the mortality predictions; ECIS, Eurostat  [16]  and the United Nation (UN) Population Division sources  [3]  e for the population data.
Data sources To predict the estimated values of cancer burden, we considered regional and national cancer incidence, mortality, and population data broken down by sex and 18 age groups (0e4, 5e9,. , 80e84, 85 years and over). For the national incidence predictions, subnational and national incidence data from 2003 to 2018 were provided by 151 European cancer registries (20 national and 131 regional registries) participating in two studies: (a) IARC, CI5-XI  [14] , and (b) ENCR-JRC Cancer Incidence and Mortality in Europe [https://encr. eu/encrjrc-project] (from which the ECIS data are compiled). Regional mortality data were also provided by most of the cancer registries for the equivalent years of incidence data. For the national mortality predictions, the national mortality data from 2004 to 2018 were retrieved from the World Health Organisation (WHO) database  [15]  Other incidence and mortality data were collected for selected countries.
Aggregated incidence data for the Nordic European countries were taken from the NORDCAN database of the Association of Nordic Cancer Registries  [17] ; for Luxembourg, data were taken from a published report  [18] ; for the Russian Federation, the incidence data (up to 2018) were extracted from the series of reports published by the Moscow Research Oncological Institute  [19] ; other incidence and mortality data were also considered from a published report of the Montenegro cancer registry  [20]. The population data were provided by cancer registries, or retrieved from Eurostat  [16]  and the United Nations (UN)  [3]. In addition, projected populations for the year 2020 were taken from either Eurostat or UN data. In the following subsections of Materials and Methods, we provide the key definitions and methods used in compiling the European cancer estimates; further details are provided in  Ferlay et al.
Method 1 e estimates based on national or local incidence data  (23 countries). For the large majority of   cases in which countries have either national incidence data or above 60% population coverage, the general prediction method described in section 2. 2 was applied. 2. 2. 2. 2. Method 2A e estimates based on most recent incidence national or local incidence data (2 countries). For toosparse incidence data in the time interval used as a basis for prediction, the most recent incidence rates were considered, where available, for at least three consecutive years and with at least 20 cases recorded per year. These were then applied to the 2020-projected population figures to obtain the 2020 estimates. For Slovakia, despite the availability of a long time series, the most recent available rates (2001e2010) were applied to the projected populations to achieve the most robust result (method 2A) for all the cancer sites.
2. 2. 2. 3. Method 3A e estimates based on local incidence and mortality data (7 countries). For countries with regional registries, the estimated national sex-, site-and age- specific incidence estimates (I N ) were obtained using corresponding aggregated regional incidence (I R ) and national mortality (M N ) estimates. Under the assumption that regional incidence to mortality ratios are similar to the national ones we derive the formula: I N ZM N Ã ðI R =M R Þ When dealing with regional registries, aggregated mortality to incidence ratios were calculated using a weighted average over the registries, with weights corresponding to the square roots of the registries' populations to guarantee the representativeness of the aggregation. A Poisson regression model was then applied to obtain the I R /M R ratios, with categorical terms for sex and age.
e. Bulgaria, Romania (2 registries) and Serbia, or Southern Europe countries, considering Croatia, Cyprus, Italy (33 registries), Malta, Slovenia and Spain (14 registries). For Luxembourg, the data of 16 cancer registries in Belgium and France were considered for the computation of the neighbouring I R /M R ratios applied to the 5year national mortality data (2012e2016). Such results were then completed using the data of the Luxembourg pathology register  [23]  according to the procedure previously applied  [12].
Completeness of national mortality data (percent), coverage of incidence data (percent) and methods of estimation. Malta United Kingdom 100 100 1/1 1/3 1 1 2007e2016 b 2020 2007e2016 b 2020 Cervical and corpus uteri 100 100 1 1 2004e2013 b 2020 2004e2013 b 2020 Non-melanoma skin cancers estimated Table 1 (continued ) Moldova Table 1 (continued ) 83 1/0 1 2009e2018 b 2020 cancers estimated using 0 3B Estimated from mortality from Northern Ireland and Scotland Country Albania Austria Belarus Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus h Czechia Denmark Estonia Finland France Luxembourg Lithuania Latvia Ukraine Federation Country Germany Greece Hungary Iceland Ireland Italy Montenegro l Netherlands North Macedonia Norway Poland Portugal Romania Russian Country National cancer mortality data from 2004 to 2018 were extracted from WHO mortality database, unless otherwise specified.
03 mbar for 8 h, then soaked in Calgon (buffered sodium hexametaphosphate [NaPO 3 ] 6 ) for 24 h. Samples were then washed through a 63 µm sieve with de-ionized water to separate the fine fraction from the sand-sized fraction. The recovered sand-sized fraction was then covered in Calgon again and placed on a shaker table for 2 h. Samples were then washed over a 63 µm sieve with de-ionized water followed by methanol to evaporate remaining water and dried in an oven at 50°C. Coarse fraction (%CF) data were obtained during processing of the samples by weighing the dried bulk sediment (bulk w ) after freeze drying and prior to soaking with Calgon. The >63 µm fraction (coarse fraction) was then weighed once dried (CF w ). The %CF was then calculated using the following formula: %CF CFw bulk w      100 (1) The >63 µm CF in this core is made up almost entirely of foraminifera.
Foraminiferal Stable Isotope Analysis Paired planktic and benthic foraminiferal stable carbon and oxygen isotopes (δ 13 C and δ 18 O) were analyzed for insights into surface and bottom water oceanography. A total of 166 multi-specimen samples were analyzed between the depths 233. 99-280. 11 mbsf. δ 13 C and δ 18 O data were generated at the British Geological Survey using an IsoPrime 100 Gas Source Isotope Ratio Mass Spectrometer with Multiprep Device. Average analytical error (2σ) was 0. 06‰ for δ 13 C and 0. 08‰ for δ 18 O (n = 25) for the standard (KCM) analyzed at the same time. Data are reported relative to the Vienna Pee Dee Belemnite scale.
Core Scanning X-Ray Fluorescence (XRF) Core scanning XRF was undertaken at Woods Hole Oceanographic Institution using a Cox Analytical ITRAX with a Mo X-Ray tube set to 60 kV and 30 mA with a 15 s count time. The XRF spectra were interpreted, and the peak areas were quantified using software Q-Spec. A suite of 13 elements were analyzed at 4 mm resolution. Raw counts of Ca are calibrated against values of CaCO 3 wt% (this study; see below and Figure  S3 ), thus absolute counts detected in XRF have been used quantitatively. Other elements were not calibrated against absolute concentrations of the same sediment samples, therefore only ratios of XRF elemental count data are used. Core scanning XRF-derived elemental ratio of Ti/Ca was used as a proxy for terrigenous input versus biogenic carbonate, Mn/Ti and Mn/Fe were used as proxies for redox conditions (see  Rothwell & Croudace, 2015  and references therein).
Terrigenous Flux The rate of terrigenous mass accumulation (MAR) can be used to reveal insights into non-carbonate related sedimentation at a site, assuming minimum contribution from biogenic silica. The terrigenous MAR was calculated using high-resolution CaCO 3 (wt%), dry bulk density (DBD), and linear sedimentation rate (LSR) as follows:   3 100 CaCO DBD LSR 100 MAR          (7) High-resolution CaCO 3 (wt%) was calculated (Figure  S4 ; R 2 = 0. 9558) using Ca counts from core-scanning XRF calibrated to discrete sample measurements of CaCO 3 (wt%) performed at WHOI using JY Ultima C ICP-OES following methodology from  Bertrand et al. (2012) ; (based on  Murray et al. , 2000). High-resolution DBD was calculated using discrete sample measurements of DBD correlated to high-resolution multi-sensor core logging density measurements from shipboard data (Figure  S4 ; R 2 = 0.
4 ) against other contemporaneous high-resolution data (raw carbon isotope data). ODP Site 1218  (Pälike et al. , 2006; paleodepth ∼4,200 mbsl)  in the eastern equatorial Pacific in orange and light blue. ODP Site 1264  (Liebrand et al. , 2016 ; paleodepth ∼2,500 mbsl) in the equatorial Atlantic in pink and green. Carbon isotopes show good correlation between the records, with offsets in absolute values likely due to different ages of deep-water masses in each ocean basin. Oxygen isotopes show generally similar overall values between sites, with some differences in trends, for example, in the late Oligocene interval where data from the Arabian Sea (this study) remain more positive relative to the Pacific and Atlantic datasets.
, 2019) , and a temporary alteration in wind direction drove dust provenance changes in the equatorial Pacific  (Hyeong et al. , 2014). Due to the low-resolution nature of both of these datasets, exact timings for these changes are not well-constrained, however both are associated with the early OMT glaciation event ("Mi-1" event;  Miller et al. , 1991). Hyeong et al. (2014)  propose the southward shift of the ITCZ may have been caused by an increased Northern Hemisphere cooling rate. This is supported by low-resolution TEX 86 -derived SST datasets compiled from the Atlantic during the early phase of the OMT (Figure  10 ); these show a relatively warmer South Atlantic in comparison to the North Atlantic  (Guitián et al. , 2019; O'Brien et al. , 2020; Super et al. , 2018)  providing a possible mechanism for the southward ITCZ shift.
Figure5. Benthic foraminiferal stable carbon (red circles) and oxygen (blue squares) isotope data from this study (Site NGHP-01-01A; paleodepth ∼2,250 mbsl) on the new biostratigraphic age model (Figure4) against other contemporaneous high-resolution data (raw carbon isotope data). ODP Site 1218 (Pälike et al. , 2006;  paleodepth ∼4,200 mbsl)  in the eastern equatorial Pacific in orange and light blue. ODP Site 1264 (Liebrand et al. , 2016 ; paleodepth ∼2,500 mbsl) in the equatorial Atlantic in pink and green. Carbon isotopes show good correlation between the records, with offsets in absolute values likely due to different ages of deep-water masses in each ocean basin. Oxygen isotopes show generally similar overall values between sites, with some differences in trends, for example, in the late Oligocene interval where data from the Arabian Sea (this study) remain more positive relative to the Pacific and Atlantic datasets.
Acknowledgments This research forms part of a PhD study funded by the  Natural Environment Research Council (NERC) Centre for Doctoral Training in Oil & Gas  (grant number  NE/M00578X/1 ) awarded to  C. Beasley , and was also supported by a  NERC   National Environmental Isotope Facility Steering Committee grant  ( IP-1865-1118 ) awarded to  S. Kender. L. Giosan  acknowledges funding from  USSP  and  WHOI  and thanks colleagues from the  NGHP-01  expedition. C. Ullmann acknowledges funding via  NERC  grant  NE/N018508/1. We thank  Joe Stewart  and  Or Bialik  for interesting and fruitful discussions of initial datasets. Thanks also go to two anonymous reviewers and Alex Farnsworth (AE), whose comments and suggestions greatly improved the manuscript.
Limited changes in the number of significantly positive ACCs per year and per forecast system behttps://doi. org/10. 5194/wcd-2-1033-2021 Weather Clim. Dynam. , 2, 1033-1049, 2021  fore and after detrending confirm the minimal influence of the warming trend on the forecast skill. In any case, 2009-2010 stands out as the winter with the maximum number of skillful forecasts of the 20-year period for both CNRM and ECMWF systems considered either jointly (green bars) or separately (blue and red bars). Since that winter is characterized by a record-breaking neg-ative NAO index  (Cattiaux et al. , 2010) , we have computed the correlation between the yearly number of skillful forecasts and a winter mean NAO index (December to March) derived from our daily NAO index datasets. The correlation is not significant if the NAO index is computed by averaging daily NAO indices (not shown).
Despite different datasets and methodology, the NAO decorrelation compares well to other studies when keeping the whole sample  (Keeley et al. , 2009) , with a characteristic decorrelation time of 8 to 10 d. However, the decorrelation is much slower when considering only the subsample with intense NAO initial conditions. Keeley et al. (2009)  also identified a similar "shoulder" or "rebound" in the NAO decorrelation function between 10 and 30 d and find it largely related to interannual variability as opposed to intraseasonal. The decorrelation timescale and behavior are consistent when evaluated over a wide range of different NAO indices (Fig. 3b  in  Domeisen et al. , 2018). The overlap between confidence intervals indicates that the difference found is not significant beyond 10 d when the NAO index is derived from ERA5 sea-level pressure.
The composite storms had radii of maximum tangential wind roughly between 30 km and 70 km, with ARPEGE and IFS on the lower end (Figs. 10a, e ) and ICON on the upper end (Fig. 10d ). In FV3 and MPAS, the wind maximum was comparatively narrow and confined, and the radial extent of the 35 m s -1 isotach was less than 20 km (Figs. 10b, f ). Contrarily, in ICON and NICAM, the wind maximum was rather broad, and the radial extent of the 35 m s -1 isotach was greater than 50 km (Figs. 10d, g ). Differences in the radial and vertical decay rates mirror the previous discussion of storm size, that is, models in which the tangential wind decayed more slowly, such as in ICON and NICAM, were the ones that produced comparatively larger storms. Given the lack of an equivalent observational dataset, it is difficult to assess which model produced a particularly realistic tangential wind structure.
This difference may be a reflection of more or stronger rainbands in the former models. Again, it is difficult to say which models produced a particularly realistic structure because no equivalent observational dataset exists for the TCs observed during the DYAMOND period. Stern and Nolan (2009)  demonstrated that the slope of the eyewall depends on the size of the radius of maximum wind, which would explain why the eyewall updraft in IFS had a steeper slope than in UM. However, The Stern and Nolan study cannot explain the differences between models with similarly sized radii of maximum wind, such as MPAS and UM. The upper-tropospheric outflow also differed between the models, especially with regard to the altitude of the outflow maximum and the depth of the outflow layer. For instance, the outflow was comparatively deep in FV3 (Fig.