dataesr/scientific-paragraphs-categorization huggingface dataset:
Ten selected ocean reanalyses The ORA output data have been collected in a data base hosted by the Integrated Climate Data Center (ICDC) at Hamburg University 1  and are freely available. Some data were already present from previous ORA-IP studies, but many products were updated and a few new ones added for this study. Ten ORAs were selected to be compared (Table  1 ), with the most comprehensive temporal overlap over 1993-2010 consisting of all variables required for the diagnostics. The remaining ORAs were discarded due to lack of data either in terms of temporal coverage or variables. Nine ORAs have a global coverage, while one (TOPAZ4) is a regional Arctic-North Atlantic product. Of nine global ORAs, five are of European origin (all using varying versions of the NEMO ocean), three are American and one is Japanese.
Due to the inclusion of one regional Arctic reanalysis that excludes the North Pacific, the Arctic-integrated extent and area are calculated over a reduced Arctic domain closed at the Bering Strait. We use the same observational datasets as in  Chevallier et al. (2017)  to assess the realism of ORAs, while taking into account observational uncertainties. Specifically, these observational sea-ice concentration products are based on the NASATeam algorithm of the National Snow and Ice Data Centre (NSIDC;  Cavalieri et al. 1999) , from Ifremer/CERSAT using the ARTIST algorithm, and by EUMETSAT Ocean-Sea Ice Satellite Application Facilities (OSISAF). Although these three products have resolutions finer than 25 km, all data are interpolated onto the common regular grid. Sea-ice thickness (SIT hereafter) is a key diagnostic for assessing the performance of ORAs in the polar oceans.
An unrealistic reconstruction of SIT would mean that essential thermodynamic processes controlling ice growth or melt are missing, or that the dynamics of the sea-ice pack is not captured accurately, or both. A major obstacle for the assessment of SIT is the lack of observationally-based data. Unlike sea-ice concentration no large-scale and time-homogeneous records of sea-ice thickness are available. For the Arctic sea-ice thickness, most of our knowledge relies on collections of datasets from various sources (e. g. Lindsay 2010). Chevallier et al. (2017)  used estimates of sea-ice thickness from the ICESat instruments, and estimates of sea-ice volume gathered in  Zygmuntowska et al. (2014). In our study, data from the Ice Thickness Regression Procedure (ITRP) are used to analyze the ORA performance.
We selected two 2-month periods (February/March and October/November) for the comparison because the ICESat data are available in these months. The ITRP combines upward looking sonar, airborne electromagnetic, NASA operation Icebridge, and ICESat remote sensed ice thickness observations, as explained in detail by  Lindsay and Schweiger (2015). Despite the fact that the ITRP thickness data are a result of complex data processing, we believe that the ITRP is the best data set to compare models with. This is due to the following: it allows to calculate sea-ice thickness deviations per grid cell and to integrate total sea-ice volumes in the ITRP region. These metrics are calculated for the period of 2000-2012, with which the ORAs are compared, with the exception of UR025. 4 which ends in 2010.
The most comprehensive database adapted for the purpose of evaluating the Antarctic SIT of ORAs is ASPeCt  (Worby et al. 2008). This product covers the period 1981-2005 and comprises about 23,000 individual measurements made during ship voyages or helicopter campaigns in the Southern Ocean. Sea-ice thickness was estimated visually by experts onboard. It is therefore likely (1) that systematic errors are present: ships tend to circulate in thin ice, hence estimations are probably biased thin, and (  2 ) that random measurement errors are large, due to the rather simplistic method of measurement (see  Worby et al. 2008 , for further discussion). The assessment of ORAs with respect to ASPeCt should therefore be conservative and made with extreme caution, in order to not discard ORAs for the wrong reasons.
Unlike the ORA-IP dataset, the ASPeCt data is not gridded and is provided as daily and not monthly values, which complicates further the assessment. We first binned the ASPeCt data in space and time by matching each of the ∼ 23,000 ASPeCt measurements to the corresponding ORA 1 • × 1 • grid cell, year and month over 1993-2005. The num- ber of measurements varies greatly from case to case, but is generally low: in 57% of the cases (one case means one given grid cell during one given month of one given year), less than three measurements are available. We excluded these cases with too few data from our assessment, to limit the probability of detecting a mismatch by chance. For all other cases (four ASPeCt measurements or more in a given month of a given year in a given grid cell), we tested whether the ASPeCt measurements and the ORA-IP monthly mean values could be drawn from the same statistical distribution.
Mixed layer depth The oceanic mixed layer constitutes the interface between the atmosphere and the interior of the ocean. This layer is where all dynamic, thermodynamic and biogeochemical air-sea exchanges take place, and where the world's deep water masses acquire their properties (e. g. de Boyer  Montégut et al. 2004; Holte and Talley 2009). As the MLD is a relevant physical index of the vertical mixing intensity in the upper ocean  (Toyoda et al. 2017a ), the MLDs simulated by the ORAs are evaluated against two observationbased products. These are the Monthly Isopycnal and Mixed-layer Ocean Climatology for the Arctic (MIMOC;  Schmidtko et al. 2013 ) and a recently published Southern Ocean mixed layer climatology  (Pellichero et al. 2017). These products are both based on temperature and salinity profiles from ship observations archived in the World Ocean Database, as well as from float data from the Argo international program.
In addition, MIMOC includes data recorded by ice-tethered profilers in the Arctic Ocean, while  Pellichero et al. (2017)  use observations from animal-borne sensor programs in the Southern Ocean  (Roquet et al. 2017 ). These contemporary sources provide an unprecedented data coverage of the sea-ice regions over the entire seasonal cycle. Both climatologies are constructed using an objective mapping of the MLDs computed from instantaneous profiles with the Holte and Talley (2009) algorithm. By contrast, reanalysis MLDs are obtained from monthly mean temperature and salinity fields, using a density threshold of 0. 03 kg/m 3 with respect to the value at 10 m depth. As noted by  de Boyer Montégut et al. (2004) , MLDs computed from monthly, hence smoother, profiles can be underestimated approximately by 10-20 m compared to those based on instantaneous profiles.
Due to its regional Arctic domain, TOPAZ4 model boundary is located in the Bering Strait where a volume transport of 0. 7 Sv to the Arctic is prescribed. As temperature and salinity are not prescribed, we decided it is not meaningful to estimate heat and freshwater transports in the Bering Strait for TOPAZ4. Therefore these TOPAZ4 quantities, and consequently the net Arctic heat and freshwater fluxes, were excluded from the MMM. When calculating the ocean transports from the ORA results, the Hudson Strait in the Canadian Arctic Archipelago is omitted, as is the part north of the Barents Sea Opening, i. e. , the opening between Bear Island and Spitsbergen Island. These choices make the ORA data more easily compared with the observed transports across the same transects. Some of the modelled ocean transports are calculated based on aggregated data which are interpolated in space and averaged in time, excluding short-term variability.
Specifically, domain averages are limited by their depth so that in the Arctic the ocean grid points deeper than 500 m are included, while in the Antarctic the limit was 1000 m. The northern limit of the Antarctic basin is chosen as to ensure that the largest fraction of the area is covered with sea ice in (1) OHC = ∫ -H (z)dz, (2) OSC = ∫ -H S(z)dz, winter, and therefore represents a polar marine environment. All ten ORAs and three observational products (Sumata, WOA13 and EN4. 2. 0. g10) were interpolated to a common 1 • horizontal latitude-longitude grid, which is identical to the WOA13 grid, before the calculation of regionally averaged hydrographic profiles. As the ORA database does not provide land-sea masks of individual ORAs, we assumed the WOA13 land-sea mask available from the WOA13 website.
MOVE-G2i, on the other hand, has a relatively small seasonal amplitude. The vertical profiles of temperature from the reanalysis products and observational datasets that are averaged over the Antarctic region (Fig. 1 ) are evaluated against the WOA13 temperature profiles in Fig. 24. As noted in Sect. 3. 7, the water column is divided into five layers: 0-100, 100-300, 300-700, 700-1500 and 1500-3000 m, and mean temperatures for these layers were computed and compared between the separate datasets and WOA13. In Fig. 24a  the EN4. 2. 0. g10 and the MMM temperatures are in fairly good agreement with the WOA13 temperature below 700 m. Between 300 and 700 m, where the water column is the warmest due to the presence of the Upper Circumpolar Deep Water (UCDW), the EN4. 2. 0. g10 temperature is almost identical to the MMM, while the MMM temperature is higher than WOA13 by ∼ 0.
1 • C. The largest temperature differences between the MMM and observational datasets occur between 100 and 300 m, where the MMM has a warm bias of 0. 4 and 0. 2 • C relative to WOA13 and EN4. 2. 0. g10, respectively. This possibly indicates a larger fraction of winter water in the observations. In the 0-100 m layer, where the water is the coldest, the MMM is also warmer than observed values, but the deviations are not as large as in the layer below. In the 0-100 m layer the WOA13 and EN4. 2. 0. g10 temperatures are colder than the MMM temperature by 0. 25 and 0. 1 • C, respectively. Figure  24b  shows the vertical profiles of basin-wide mean and standard deviation of temperature differences between individual ORAs, MMM and EN4. 2. 0. g10 from the WOA13 temperature values. The ORA mean difference range and their standard deviations are relatively small in the surface layer with all the individual reanalyses differing by < 0.
The largest deviations in water mass properties occur for GloSea5-GO5 and GLORYS2v4, which have considerably higher temperature than the MMM and observations, and for GECCO2 which has a much lower potential density than the MMM, by 0. 12 kg m -3 , associated with the exceptionally low salinity in this layer. In the 300-700 m layer, where the presence of the Upper Circumpolar Deep Water (UCDW) leads to the highest temperatures and salinities, the agreement is good between EN4. 2. 0. g10, WOA13 and the MMM, with a slightly higher potential density in WOA13. In this layer, GECCO2 is the only dataset that has considerable potential density deviation from the MMM, due to its low salinity. In the 700-1500 m layer, mainly occupied by the Lower Circumpolar Deep Water (LCDW), and in the deep layers below 1500 m, the densities are even closer between EN4.
Similarly, when compared to Arctic ITRP observations, the ORA sea-ice volumes tend to have cancelling positive and negative biases and, as a result, the MMM sea-ice volume is surprisingly close to the ITRP sea-ice volume (Table  3 ). Perhaps analogously, the ORA ensemble sea-ice thickness cannot be deemed inconsistent with the ASPeCt data in the Antarctic, while most individual ORAs are themselves inconsistent with this observational data set. This points towards the important role of model error in the misrepresentation of sea-ice thickness. In other words, the spread of the ORAs appears large enough to reflect what is uncertain in the experimental setup and in the models used, but this data set is not sufficient to provide trustful reconstructions and estimations of past Antarctic sea-ice thickness.
The MMM heat transport through the Fram Strait is lower than observed, its heat transport through the BSO is close to the observed and its AW layer is too cold. This indicates that in the ORAs, probably due to their better resolution compared to CORE-II, and to data assimilation, the transport over the St. Anna Trough is more realistic while, on average, their Fram Strait heat fluxes remain too cold resulting in a cold Arctic Ocean. Of individual products, GECCO2 and MOVE-G2i show excessive transports through the BSO but very low in the Fram Strait (Fig. 8 ). However, their Arctic Oceans are much warmer than observed. For example, the MOVE-G2i Eurasian basin mean temperature is the highest among the ORAs in the top 700 m (Fig. 13 ). In GECCO2 the warm Arctic can be explained by its extremely warm Nordic Seas and high BSO heat transport.
However, such a study would require more detailed diagnostics than currently available in the ORA-IP database, otherwise the results would not significantly differ from those of  Chevallier et al. (2017). Ideally, we would like to separate the impact of data assimilation from, for example, model physics, which would require the use of analysis increments as a measure. Carrying out such an approach can be considered as an in-depth assessement of a few ORAs, but is clearly beyond the scope of our study. In general, the earlier ORA-IP studies, except one, did not investigate the impact of analysis increments presumably for similar reasons. Based on six ORAs, where data were available for calculations,  Valdivieso et al. (2014)  found that the analysis increments were compensating for the inadequacies of the atmospheric reanalysis used to force the ORA, but the ORA ensemble still gave the best estimates of the oceanic and sea-ice quantities.
Acknowledgements We acknowledge  Dr. Benjamin Rabe  and the two anonymous reviewers for their comments that significantly improved the manuscript. EU- COST EOS-1402 Ocean Synthesis action  is acknowledged for their support, in particular to assist the organisation of the Polar ORA-IP meetings, both physical and virtual, which were crucial for the study. Work of Uotila was supported by the  Finnish Academy  (Grants  264358  and  283034 ) and by the  EU MCSA  grant  707262-LAWINE. Chevallier, Fučkar,  Haines  and Massonnet have received funding from the  European Union 's  Horizon 2020 Research and Innovation programme  through Grant agreement No. 727862  APPLICATE. Fučkar was a Juan de la Cierva-incorporacion fellow supported by the  Spanish government. Goosse is a research director and Massonnet a post-doctoral researcher with the  FRS/FNRS, Belgium.
VUDS Survey summary Our galaxy sample is drawn from the VIMOS Ultra Deep Survey (VUDS). Details about the survey strategy, target selection, as well as data processing and redshift measurements are presented in Le  Fèvre et al. (2015). Below we provide only a brief summary of these survey features, that are relevant to the study of the galaxy clustering presented in this paper. VUDS is a spectroscopic survey targeting ∼ 10 000 galaxies in the redshift range 2 < z < 6+. The survey covers a total area of ∼1 deg 2 across three independent fields (see Fig. 1 ), thus reducing the effect of cosmic variance, which is important for galaxy clustering measurements. The majority (∼ 88%) of targets are selected based on photometric redshifts (z phot + 1σ 2. 4) derived from deep multi-band photometry available for the VUDS fields, and supplemented with the targets selected by various magnitude and colour-colour criteria (mainly Lyman Break Galaxies, LBGs).
2015). The multiwavelength photometry is used to compute absolute magnitudes from the SED fitting using the 'Le Phare' code  (Arnouts et al. 1999; Ilbert et al. 2006) , as described in detail by  Ilbert et al. (2005)  and references therein (see also  Tasca et al. 2015). Stellar masses are measured using GOSSIP+ (Galaxy Observed-Simulated SED Interactive Program) software, which performs a joint fitting of both spectroscopy and multiwavelengths photometry data with stellar population models, as described in detail by  Thomas et al. (2016). We note that this stellar mass measurement method differs from the commonly used ones based on, e. g. , SED fitting on the multi-wavelength photometry. We decided to use the GOSSIP+ stellar masses, due to the larger number of reliable M measurements available for the VUDS sample.
In practice, we therefore limit our measurement to scales for which the number of galaxy-galaxy pairs in VUDS data is sufficient to measure correlation function with uncertainties that do not exceed the value of w p (r p ), and are not affected by volume effects. 3 for the error estimation method) denote measurements of the composite correlation function for different luminosity (circles) and stellar mass (squares) sub-samples selected from VUDS survey in the redshift range 2 < z < 3. 5. For clarity, offsets are applied both to the data points and best-fitting curves of the w p (r p ), i. e. , the values of w p (r p ) and associated best-fits for galaxy sub-samples with increasing luminosity and stellar masses have been staggered by 0. 5 dex each. Error contours on the fit parameters are obtained taking into account the full covariance matrix.
Acknowledgements. This work is supported by funding from the  European Research Council Advanced  Grant  ERC-2010-AdG-268107-EARLY  and by  INAF  Grants  PRIN 2010 ,  PRIN 2012  and  PICS 2013. AD is supported by the  Polish National Science Centre  grant  UMO-2015/17/D/ST9/02121. This work is based on data products made available at the  CESAM data centre, Laboratoire d'Astrophysique de Marseille. This work partly uses observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
In the four first scenarios in Table  2 , stakeholders implicitly apply grandfathering to marine habitat destruction by setting the reference year to 1979, i. e. the year the Birds Directive was enacted to preserve bird habitats, some of which also serve as fish nursery habitats. This means any economic activity that destroyed nursery areas before the Birds Directive was enacted would be exempted from restoring those nurseries. However, in the 'Three stage scenario with cooperation and grandfathering mechanisms' -the last one in Table  2  -, we set the grandfathering reference year to 1992, i. e. the year the Habitat Directive was enacted. As the number of responsibility stages increases, the transaction cost may also increase since more producers and consumers are included  (OECD, 2004). With this in mind, the restoration scenarios exclusively consider firms with annual gross earnings over Me 2, reducing the scope of responsible producers from 215,954 total companies in the Seine estuary to 4393 companies (our own calculation in the Orbis database produced by the Bureau van Dijk -A Moody's Analytics Company, 2014).
life span of sector j since its creation date up to 2012 calculated across all companies of sector j as given by the Orbis database produced by the Bureau van Dijk -A Moody's Analytics Company (2014); ē2012 j is the average life span calculated across all the n sectors n j=1 e 2012 j /n. As a result, a sector with companies older than the average will have a higher coefficient j value than a sector made of younger companies. ψ adjust. = (ψ No historical factor sectors /ψ With historical factor sectors.
Co-immunoprecipitation experiments To study the physical interaction between PfuPCNA and PfuMR wt or PIP, 8. 33 g of anti-P. abyssi (Pab) PCNA polyclonal antibody was immobilized onto 1. 5 mg of magnetic Dynabeads Protein A (ThermoFischer). Subsequently, antibodies were covalently anchored using 57 g of BS3 crosslinker (Thermo Scientific); such an amount of beads was determined in order to specifically bind 1 g of PfuPCNA input (data not shown). In a 20 l reaction volume, 1 g PfuPCNA was incubated for 30 min at 4 • C with 10 g PfuMR wt or PIP complexes in binding buffer (25 mM HEPES pH 7. 0, 150 mM NaCl, 1 mM DTT, 0. 05% Tween 20). The resulting protein complexes were trapped by anti-PCNA Dynabeads over 10 min at 4 • C. Beads were washed three times with binding buffer (100 l) before final elution for 10 min at 95 • C in denaturating XT loading buffer (Bio-Rad).
SPR experiments Data were obtained using a Reichert SR7000DC spectrometer instrument (Reichert Inc. , Buffalo, NY, USA). The running buffer was 25 mM HEPES pH 7. 0, 300 mM NaCl, 1 mM DTT and 0. 05% Tween 20, and flow rate was 25 l/min. PfuPCNA was immobilized on a mixed self-assembled monolayer (10% C11-(OEG)6-COOH: 90% C11-(OEG)3-OH), Reichert Inc. ) via classic amine coupling chemistry and 25 nM of PfuMR wt or PIP complexes were injected over the PfuPCNA surface at 25 • C. The chip was regenerated after serial injections of 100 mM H 3 PO 4 (3 × 30 s). Each curve displayed was double referenced with a set of blank buffer injections. S1B ). 1 g of protein was loaded on SDS-PAGE as Input. IP corresponds to the immunoprecipitation assays in presence of beads coated with PCNA antibodies. Fraction bound to the beads were analysed by Coomassie blue staining.
Although this motif lacks the glutamine conserved residues, these features strongly suggest that it could act as a hydrophobic anchor on PCNA. We then looked at occurrence of this motif in Mre11 sequences among species of the order Thermococcales. Remarkably, all available sequences displayed this putative PCNA-interacting motif in the C-terminal region (Supplementary Figure  S2 ). From this alignment, we could derive a pattern [PK]-x-[KRNA]-x-[GSPNK]-x(1,3)- [IL]-x(2)-[WFY]-[ILV] ) for a motif search using Scanprosite on archaeal protein sequences from Swiss-prot and TrEMBL databases. This pattern was detected in Mre11 sequences from the order Thermococcales and from an Archeoglobale, Archeoglobus fulgidus. We also identified that Hel308/Hjm archaeal members of helicase superfamily 2 possessed a similar motif in the extreme C-terminal region (Supplementary Figure  S2 ).
As described for the PIP motif of RFC from P. furiosus, these residues could establish electrostatic interactions with the negatively charged surface of the PCNA C-terminal tail and compensate for the absence of glutamine residue  (59, 60). These results suggest that the peptide detected on PfuMre11 sequences is a variation on a theme of the canonical PIP motif, lacking the otherwise conserved glutamine residue, and mediates interaction between PfuPCNA and the PfuMR complex Remarkably, the putative PCNA interacting motif is conserved among Mre11 sequences of all Thermococcales species and of Archaeoglobus fulgidus. Similar PCNA interaction motifs were not found in other archaea or in eukaryotic Mre11 sequences. However, a search for a canon- ical PIP motif (Q-x(2)-[LIVM]-x(2)-[FYW]-[FYWLIVM] ) in generic databases Swiss-Prot and TrEMBL, using Mre11 as description filter, identified Mre11 sequences from the orders Methanosarcinales and Halobacteriales as potentially harbouring a canonical PIP motif at the extreme Cterminus region (data not shown).
ACKNOWLEDGEMENTS We thank  Tanya Paull ,  John Tainer  and  Bernard Connolly  for sending clones and we are grateful to  Gareth Williams  for providing proteins and for helpful discussions. Author Contributions: G. H. , L. Y. , S. L. , E. H. , B. C. O. , D. F. designed the experiments; G. H. , Y. L. , S. L. , R. D. , D. K. P. , A. B. , P. F. P. performed the experiments; G. H. , D. F. , Y. L. , S. L. analysed the data; G. H. , D. F. , Y. L. wrote the paper; and D. F. conceived and directed the study. FUNDING Ifremer; CNRS;  University of Western Britany. This work was supported by the  French "Agence Nationale pour la Recherche"  [ ANR-16-CE12-0016  to B. C. O. and D. F. ]. Funding for open access charge: Ifremer.