dataesr/scientific-paragraphs-categorization huggingface dataset:
PI sea ice For the present-day we have satellite and in situ observations with which to evaluate the models. The use of present-day sea ice data implies that we might expect the simulated PI sea Clim. Past, 17, 37-62, 2021 https://doi. org/10. 5194/cp-17-37-2021 ice to be generally somewhat larger than the observed mean. Indeed the atmospheric CO 2 levels for the years for which we chose the observation dataset  (1982 to 2001)  were between 340 and 370 ppm, compared to the PI level of 280 ppm. Figure  2  shows the mean seasonal cycle of the Arctic sea ice extent simulated for the PI and LIG alongside the observed Arctic sea ice extent. The summer minimum monthly MMM SIA for the PI is 6. 46 ± 1. 41×10 6 km 2 , compared to the observed 1981 to 2002 mean of 5. 65×10 6 km 2. In terms of SIE, the summer minimum for PI is 8.
89 ± 1. 41×10 6 km 2 , to be compared to the observed 7. 73×10 6 km 2. Interestingly this MMM PI area and extent is a little larger than the 1981-2002 area. The majority of the simulations show a realistic representation of the geographical extent for the summer minimum (Fig. 3 , Table  4 ), with 9 out of 16 models showing a slightly smaller area compared to the present-day observations and 7 showing an overestimated area. LOVECLIM, EC-Earth, FGOALS-g3, GISS-E2-1-G and INM-CM4-8 clearly simulate too much ice (Table  4 ). The other models generally exhibit realistic PI summer minimum ice conditions. The detail of the geographical distribution of sea ice for the models, the MMM and the NOAA_OI_v2 datasets (Fig. 3 ) confirms the results in terms of Arctic sea ice extent. Overestimations appear to be due to too much sea ice being simulated in the Barents-Kara area (LOVECLIM, FGOALS-g3, GISS-E2-1-G), in the Nordic Seas (EC-Earth, FGOALS-g3) and in Baffin Bay (LOVECLIM, INM-CM4-8, EX-Earth).
MIROC-ES2L performs rather poorly for the PI, with insufficient ice close to the continents. The other models generally match the 0. 15 isoline from the NOAA_OI_v2 dataset in a realistic manner. The winter maximum monthly MMM areas show little difference between the present-day and PI simulated areas. The MMM PI area is 15. 16 ± 1. 90×10 6 km 2 , compared to the observed 1981 to 2002 mean of 14. 44×10 6 km 2. For both the summer and winter, the simulations and observations mostly agree on the month that the minimum and maximum are attained: August-September for the minimum and February-March for the maximum for every model. Before we carry out the comparison between model results and sea ice cover reconstructions for the LIG period, we compare the results of the models for PI to the observations at the reconstruction sites (Fig.
During summer, this decrease in upward short-wave flux more than compensates the decrease in SWdn, which is maximum in August. of the symbols on the maps correspond to the observed values at each site, which are classified into three categories according to the NOAA_OI_v2 dataset: perennial cover (9 to 12 months), seasonal cover (3 to 9 months) and ice-free state (0 to 3 months). On the MMM panel, for each data site the colour of the symbol outline corresponds to the number of models simulating the observed ice cover. On the panels for individual models, the shape of the symbol depends on the observed result being below the simulated one (triangle down), above the simulated one (triangle up) or in the same category as the simulated one (circle). The number of sites for which reconstructions are equal to and above the number of months simulated by models are written at the bottom-right corner of each panel.
The albedo simulated by HadGEM3 in May and June is particularly low compared to the two other models, which explains why the SWup component peaks earlier. anomalies over the whole Arctic show that the sea ice albedo feedback is most effective in HadGEM3. In terms of cloudiness, IPSLCM6 shows differences in the properties of clouds, in terms of optical depth, between PI and LIG, but this could not be investigated due to a lack of data (thus far) for the other models. Thus we cannot tell if LIG-PI anomalies in SWdn fluxes, i. e. differences between HadGEM3's and CESM2 flux, also have a contribution due to cloud changes. The comparison to other model results (Fig. 10 ) confirm that the behaviour of HadGEM3 is unusual in terms of energy budget. It is the only model in which the anomalies in SWup and SWdn are exactly in phase and produce a much larger anomaly in total heat budget, while in other models those anomalies are not in phase and partly compensate each other.
Ocean and atmosphere circulation differences Changes in Arctic sea ice related to ocean heat transport have been found for the CESM large ensemble  (Auclair and Tremblay, 2018). The differences can then be amplified by perennial cover (9 to 12 months), seasonal cover (3 to 9 months) and ice-free state (0 to 3 months). On the MMM panel, for each data site, the colour of the symbol outline corresponds to the number of models simulating the reconstructed ice cover. On the panels for individual models, the shape of the symbol depends on the model result being below the reconstructed one (triangle down), above the reconstructed one (triangle up) or in the same category as the reconstructed one (circle). The number of data points which are above, equal and below the number of months simulated by models are written in the bottom-right corner of each panel.
North of 79 • N the sea-ice-related records are more difficult to obtain and interpret. However, the core at 81. 5 • N brings evidence of summer being probably seasonally ice-free during the LIG from two indicators: dinocysts and IP25/PIP25. The northernmost core with good chronology is located at 81. 9 • N and shows evidence of substantial (> 75 %) sea ice concentration all year round. Other cores, with debated chronologies, have not been used for modeldata comparisons in the present study. Model results from 16 models show a multi model mean (MMM) summer SIA LIG of 3. 20 ± 1. 29×10 6 km 2 , and a winter monthly MMM area of 15. 95 ± 1. 21×10 6 km 2. This is a reduction in SIA of 50 % for the minimum summer month between the PI and LIG but almost no change for the winter month MMM. Every model shows an often substantial reduction in summer sea ice between the PI and LIG.
Analysis of IPSLCM6 results shows differences in the properties of clouds, in terms of optical depth, between PI and LIG. Further work is required to identify if this is also important for other models. Changes in Arctic sea ice may also be related to ocean heat transport. Here, we have shown that ocean circulation changes occur for some (but not all) LIG simulations. Other factors that remain to be investigated include clouds and ocean heat uptake in the Arctic in the different models. Most models agree with the reconstructed year-round icefree northern North Atlantic. Model-data disagreement for the LIG occur over the Nordic Seas, close to Greenland and at the boundary with the Arctic Ocean, where many models overestimate annual mean sea ice concentration. This is not fully related to the model performance for summer.
AdV, IMV, RS and LCS compiled the sea ice dataset, and IMV produced the dataset map. DS co-planned some of the model analysis. MK, LCS, MS, MVG, AAO, PB, ECB, JC, MAC, CG, ANL, GL, KJM, LM, PM, KHN, BLOB, RO, SRB, DSyM, SST, XS, BS, RAT, EV, NKHY, WP, QZ, ZZ, TZ contributed model data. DS, DF, CB, and JS provide sea ice modelling advice. MK supervised the corrections with contributions from LCS, MS, DS, CG and BLOB. All authors read the draft and commented on the text. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Paleoclimate Modelling Intercomparison Project phase 4 (PMIP4) (CP/GMD inter-journal SI)". It is not associated with a conference. Acknowledgements. We acknowledge the QUIGS (Quaternary Interglacials working group endorsed by PAGES and PMIP) for making this comparison possible, thanks in particular to the workshop organised by this group in Cambridge, UK, in July 2019.
We are grateful to the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making their model output available, the Earth System Grid Federation (ESGF) for archiving the data and providing access to them, and the multiple funding agencies who support CMIP6 and ESGF. The Paleoclimate Modelling Intercomparison Project is thanked for coordinating the lig127k protocol and making the model-model and model-data comparisons possible within CMIP6. PMIP is endorsed by WCRP and CLIVAR. We also acknowledge NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for their optimally interpolated sea ice product, downloaded from https://doi. org/10. 5194/cp-17-37-2021 Clim. Past, 17, 37-62, 2021 Review statement.
Figure7. Sea ice annual concentration during the LIG (computed with calendar adjustment), for the multi-model mean (MMM) and for each model. The colour-filling of the symbols on the maps correspond to the reconstructed values, classified into three categories: perennial cover (9 to 12 months), seasonal cover (3 to 9 months) and ice-free state (0 to 3 months). On the MMM panel, for each data site, the colour of the symbol outline corresponds to the number of models simulating the reconstructed ice cover. On the panels for individual models, the shape of the symbol depends on the model result being below the reconstructed one (triangle down), above the reconstructed one (triangle up) or in the same category as the reconstructed one (circle). The number of data points which are above, equal and below the number of months simulated by models are written in the bottom-right corner of each panel.
Data availability. The NOAA Optimum Interpolation (OI) V2 dataset for sea ice concentration has been retrieved from https://psl. noaa. gov/data/gridded/data. noaa. oisst. v2. html (NOAA/OAR/ESRL PSL, 2019). The dataset used for the present study is the monthly dataset: ftp://ftp. cdc. noaa. gov/Datasets/noaa. oisst. v2/icec. mnmean. nc (last access: 8 December 2019). The original output data from the model simulations used in this study are available from the Earth System Grid Federation (https: //esgf-node. llnl. gov/, last access: 31 May 2020), the data repository for CMIP6 simulations, or on open repositories, as listed in this paper's companion paper by Otto-Bliesner et al. (2021).
The NorESM simulations benefitted from resources provided by UNINETT Sigma2 -the National Infrastructure for High Performance Computing and Data Storage in Norway. The ACCESS-ESM 1. 5 experiments were performed on Raijin at the NCI National Facility at the Australian National University, through awards under the National Computational Merit Allocation Scheme, the Intersect allocation scheme, and the UNSW HPC at NCI Scheme. LOVECLIM experiments were performed on UNSW HPC Katana. Ayako Abe-Ouchi, Ryouta O'ishi, and Sam Sherriff-Tadano thank JAMSTEC for use of the Earth Simulator supercomputer. Financial support. This research has been supported by the PAlaeo-Constraints on Monsoon Evolution and Dynamics (PACM-EDY) Belmont Forum project (grand no. 01LP1607A), German Federal Ministry of Education and Science (BMBF) PalMod II WP 3.
After this, the combination selection will be governed by the POCRM. The initial escalation phase as proposed by  Wages (2015)  is considered. Furthermore, the POCRM requires specification of a set of orderings that will be tried by the design. We consider six orderings as proposed by  Wages and Conaway (2013) ;  Wages (2015)  that were found to lead to good operational characteristics. The two-stage design is implemented in R-package pocrm  (Wages and Varhegyi, 2013). We also evaluate the I2D design as specified by  Riviere and others (2015). We also include the benchmark proposed by  Guo and Liu (2018)  for trials with a single binary endpoint. We refer to this benchmark as "GL". It is based on the critical information introduced by the authors that is argued to offer a middle ground between the complete information and data available in actual trial.
Acknowledgments The authors would like to thank  Dr Helen Barnett  for reading an earlier version of the manuscript, and  Beibei Guo  for providing the code implementing the GL approach. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  633567. Xavier Paoletti  is partially funded by the  Institut National du Cancer (French NCI ) grant SHS-2015 Optidose immuno project. This report is independent research supported by the  National Institute for Health Research  ( NIHR Advanced Fellowship ,  Dr Pavel Mozgunov ,  NIHR300576 ; and  Prof Jakis Senior Research Fellowship ,  NIHR-SRF-2015-08-001 ). The views expressed in this publication are those of the authors and not necessarily those of the  NHS , the  National Institute for Health Research  or the  Department of Health and Social Care (DHCS).
Background: Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans. Objectives: This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples. Methods: The approach consists of three main parts.
First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard. Results: All CECscreen database and property files are publicly available (DOI: https://doi. org/10. 5281/zenodo. 3956586). In total, 145,284 entries were aggregated from various CECs data sources.
After elimination of duplicates and curation, the pipeline produced 70,397 unique "MS-ready" structures and 66,071 unique QSARready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag. Discussion: The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.
, 2020). Of particular interest are Chemicals of Emerging Concern (CECs)  (Sauvé and Desrosiers, 2014). This label is used by various agencies around the world to classify chemicals of potential concern to human or environmental health due to expected toxicity or exposure levels, but for which very limited information related to either quantity is available. Due to this broad classification, the number of CECs is extremely high and ever changing and includes compounds with a wide variety of chemical properties and uses (e. g. pesticides, flame retardants, surfactants, pharmaceuticals, etc). Approaches such as suspect screening and effect-directed analysis (EDA) try to shed light on the presence of CECs in human biological matrices and other complex samples. The goal of this work is to develop a universal screening database of CECs for exposome research  (Pourchet et al.
, 2020; Vermeulen et al. , 2020) , with emphasis on detection of CECs in biological samples collected from human populations. An example of the possible application of this database in exposome research is included in  Pourchet et al. (2021). Due to the large number of CECs, targeted assessment of exposure levels in human samples is too costly, time consuming and often requires large sample volumes  (Wishart et al. , 2018). Chromatography techniques, including liquid chromatography (LC) or gas chromatography (GC), combined with high-resolution mass spectrometry (HRMS) provides an opportunity to screen for the presence of a wide range of CECs in human samples. A major challenge in the use of HRMS to detect CECs in human samples is the assignment of chemical identities to the several thousands of mass spectral signals ("chemical features") that are detected in a typical non-targeted HRMS analysis.
While identification of these chemical features requires validation with authentic standards (i. e. , comparison of retention times and MS/MS spectra), etc. (Schymanski et al. , 2014) , an initial annotation that provides potential chemical identities is possible by comparing the accurate mass from the detected chemical features to a database that contains exact masses of known compounds. Although this technique requires a priori information on compounds that might be present, this is a useful approach within exposome research to 'zoom in' on a specific compound class/group of compounds of interest and may facilitate the selection of relevant features to focus on for identification. Further insight into the biological impact of the annotated compounds can be acquired by combining this approach with other tools such as the fully untargeted screening of the metabolome.
However, many chemical features will remain unannotated and subsequent steps are needed to identify "unknown unknowns" specifically. Although many databases exist, few are suitable for the annotation of CECs in biological samples. Metabolomics databases, such as HMDB,  (Wishart et al. , 2018; Wishart et al. , 2007)  the Kyoto Encyclopedia of Genes and Genomes (KEGG)  (Kanehisa and Goto, 2000)  and METLIN  (Guijas et al. , 2018) , primarily incorporate compounds that have been reported in literature. Both HMDB and KEGG were primarily designed as a resource for compounds present in the human endogenous metabolome, while METLIN includes both endogenous metabolites and environmental chemicals, including >700 000 chemicals from the US EPA "Distributed Structure-Searchable Toxicity (DSSTox)" database.
(Grulke et al. , 2019)  The U. S. Environmental Protection Agency has created the CompTox Chemicals Dashboard  (Williams et al. , 2017) , which combines physicochemical properties, reference counts and a wide-range of additional meta-data for over 882,000 chemicals, providing one of most comprehensive chemical databases currently available for exposome research. The suspect lists created under the NORMAN network (https://www. norman-network. com/nds/SLE/) also provides a large source of potential CECs, however these lists are mostly focused on environmental monitoring. Additional databases include PubChem  (Kim et al. , 2019)  and ChemSpider  (Pence and Williams, 2010) , both of which contain information on millions of chemical structures; however, both PubChem and ChemSpider suffer from the potential for false-positive matches, due to chemical entries that have an extremely low probability of representing true environmental exposures.
While all of these databases provide critical resources for annotation of non-targeted HRMS features, the large number of chemicals, focus on endogenous metabolites or chemicals found in environmental samples, and limited ability to define compounds as CECs limits application for annotation of CECs in biological samples. Two other databases, the Blood Exposome Database  (Barupal and Fiehn, 2019)  and the Exposome Explorer  (Neveu et al. , 2017) , focus specifically on compounds present in the exposome. The Exposome Explorer (now included in HMDB v4. 0) contains a systematically collected and manually curated collection of 908 compounds that are reported in the literature as measured in human samples. The highquality standards of the Exposome Explorer, due to manual curation, have resulted in the small scale.
The Blood Exposome Database is larger (n = 65,957) and incorporates a text mining approach to include potentially relevant information from the literature. However, this database is tailor made to blood only, not other matrices such as urine and includes many endogenous compounds. Although a large number of data sources dealing with CECs exist we believe there is still need for a comprehensive annotation database for CECs focused on screening in human matrices specifically. In this work, performed in the frame of the Human Biomonitoring for Europe (HBM4EU) initiative (https://www. hbm4eu. eu/), CECscreen, a database of CECs including their predicted Phase I metabolites that can be used as an annotation source in exposome research is introduced. This database is a compilation of existing data sources dealing with CECs considered to be important for screening in human samples and includes annotation databases from the environmental field and regulatory agencies among others.
Furthermore, to ensure applicability for annotation in HRMS data, curation and standardization of the retrieved CECs are performed in the form of "MS-ready" structures  (McEachran et al. , 2018). The use of "MS-ready" structures help link the common substance form to the form detected by HRMS in annotation databases. As most CECs will undergo metabolism in the human body and are therefore likely more prominently present as metabolite(s) than as a parent compound, the inclusion of Phase I metabolites would also improve the applicability of this database as an annotation source in human samples. The database was populated by collating a large number of lists of CECs produced by (regulatory) agencies and academic initiatives around the world. We describe here the applied qualitative consolidation and harmonization approach, steps that were taken to make the database "MS-ready", identification of relevant metadata, and generation of predicted metabolites.
Identification and compilation of CECs data sources A general overview of the steps included in the curation work of this database is provided in Fig. 1. As an initial screening, data sources containing CECs were identified in a questionnaire in 2017 by members of the HBM4EU initiative. Compound name, Chemicals Abstract Service (CAS) number, and Simplified Molecular-Input Line Entry Specification (SMILES, when available) were extracted from the individual databases. Databases were merged based on CAS number. Using the CAS number as input, the "webchem" package  (Szöcs et al. , 2020)  in R was used to retrieve missing SMILES. Entries for which no SMILES could be retrieved were excluded from the list.