dataesr/scientific-paragraphs-categorization huggingface dataset:
Data In this article we follow the experimental framework proposed in the Experiment 2a of the European COST (Cooperation in Science and Technology) action VALUE  (Maraun et al. , 2015) , designed to assess the suitability of different SD methods to produce local climate change scenarios. We perform downscaling of daily precipitation at the 83 representative locations used in  Legasa et al. (2022) , which are distributed across Europe (12° West-32° East, 36° North-72° North, see Figure  S1  in Supporting Information S1). We build on the PP approach, that is, the three SD techniques considered are trained with ERA-Interim reanalysis  (Dee et al. , 2011)  as large-scale predictors and observed precipitation from ECA&D  (European Climate Assessment & Dataset project, Klein Tank et al. , 2002)  as local-scale predictand.
13039/501100011033 and by the European Union NextGenera-tionEU/PRTR. R. Manzanas acknowledges support from the R&D project "Contribución a la nueva generación de proyecciones climáticas regionales de CORDEX mediante técnicas dinámicas y estadísticas" (CORDyS: PID2020-116595RB-I00). M. Vrac and S. Thao acknowledge support from the H2020 funded project XAIDA with the Grant Agreement number 101003469, and from the COESION project funded by the French National program LEFE (Les Enveloppes Fluides et l'Environnement). Additionally, M. N. Legasa acknowledges partial funding by the French embassy in Spain ("Convocatoria de proyectos científicos de la Embajada de Francia en España para el año 2022").
Data Availability Statement All the data used in this work (ECA&D observed rainfall, ERA-Interim and EC-Earth predictors), are publicly available and can be downloaded from http://www. value-cost. eu/data. The R (R Core Team, 2020) packages downscaleR  (Bedia et al. (2020) , https://github. com/SantanderMetGroup/downscaleR), downscaleR. keras (Baño-Medina et al. (2021) , https://github. com/SantanderMetGroup/downscaleR. keras) and RandomForestDist  (Legasa et al. (2022) , https://github. com/MNLR/RandomForestDist) were used, respectively, to train the generalized linear models, convolutional neural networks and a posteriori random forests used in this work.
Data collection The questionnaire collected information regarding socio-demographic characteristics, substance use history, current drug use, DCR attendance, drug-use-related risk practices and self-reported HIV and HCV serostatus at each follow-up. The data collection tools were partly inspired by an evaluation of the Vancouver Downtown Eastside DCR (Insite) and the Vancouver Injection Drug User Study. We used the blood-borne virus transmission risk assessment to collect information regarding risk practices  [26]. The main variables are described in the Supporting information.
If the IMR variable was significant (P-value < 0. 05) in the multivariable model, the Heckman method was used to correct for nonrandomization bias; otherwise, a standard mixed probit regression method was used with a similar selection procedure for the explanatory variables, as presented previously. For the present analyses, we used data from the baseline (month 0) and months 6 and 12. Data from the month 3 questionnaire were not taken into account in the analysis, because the covariates were not the same as in the months 6 and 12 questionnaires. We also described attrition rates at months 6 and 12 and compared the baseline characteristics, including the three main outcomes, between participants who were lost to follow-up at month 12 and those who were not. We used a specific sample selection in order to select only participants and observations eligible or relevant for each analysis, as follows.
ACKNOWLEDGEMENTS This project was funded by the  French Addiction Government Agency Mission Interministérielle de Lutte contre les Drogues et les Conduites Addictives  (the inter- ministerial mission that fights against drugs and addictive practices ). The funding sponsors had no role in the design of the study and had no role in data collection, analysis or interpretation of the data. They were not involved in the preparation, review or approval of this manuscript. We would like to thank all the research participants and the members of the COSINUS scientific committee ( Henri-Jean Aubin ,  Patrizia Carrieri ,  Nerkassen Chau ,  Jean-Marie Danion ,  Maurice Dematteis ,  Laurent Karila  and  Thomas Kerr ), the Ethical Review Committee, the  ITMO-Public Health at Inserm  ( Rémy Slama  and  Charles Persoz ), the  French Institute for Public Health Research (IRESP ) ( Rémy Slama  and  Marion Cipriano ) and the IRESP scientific committee ( Marc Bardou ,  Christian Ben Lakhdar ,  Eric Breton ,  Olivier Cottencin ,  Helene Donnadieu-Rigole ,  Xavier Laqueille ,  Jennifer O'Loughlin ,  Christophe Tzourio  and  Frank Zobel ).
Acknowledgements This work was funded by the  Max Planck Society , the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant No. ERC-2015-CoG-682843 ), the  German Research Foundation (DFG)  within the  Emmy Noether program  (Grant No. RE 3977/1 ), through  Projektnummer 18208777-SFB 951  " Hybrid Inorganic/Organic Systems for Opto-Electronics (HIOS) " ( CRC 951  project B12, M. S. , D. C. , A. K. ), and the  SFB/TRR 227 "Ultrafast 264 Spin Dynamics"  (projects  A09  and  B07 ). S. B. acknowledges financial support from the  NSERC- Banting Postdoctoral Fellowships Program. M. D. acknowledges financial support from the  Göran Gustafssons Foundation. K. W. and T. T. acknowledge support from the  Elemental Strategy Initiative  conducted by the  MEXT, Japan  (Grant Number  JPMXP0112101001 ) and  JSPS KAKENHI  (Grant Numbers  19H05790  and  JP20H00354 ).
ACKNOWLEDGEMENTS The participation of all members of the  EFC Working Party 11-Task Force 'Corrosion  of steel in alkali-activated materials' in meetings and discussions is greatly appreciated. Participation of  Shishir Mundra  was supported by the  Swiss National Science Foundation  ( PP00P2_194812 ), and  Susan A. Bernal  was sponsored by the  UK Engineering and Physical Sciences Research Council (EPSRC)  via the  Early Career Fellowship   EP/R001642/1. Open Access funding enabled and organized by Projekt DEAL.
| Diversity of species present in EACs: CITES, International Union for Conservation of Nature's Red List, and invasive species status For each species, we recorded its status on the International Union for Conservation of Nature (IUCN) Red List of Threatened Species  (IUCN, 2021 , Version 2021-3). We then compared the species listed under the CITES and imported into Japan during 1975-2019 to the species present in EACs, with a focus on the 2010-2019 period. We retrieved data for trade transactions involving live CITES-listed animal species imported in Japan from the CITES Trade Database (http://trade. cites. org). These records are derived from the mandatory annual reports compiled by the CITES parties (183 parties in 2021). We only considered transactions classified as "live" under the key "trade" term.
CITES categorizes species into three appendices based on information available to assess how likely a species is threatened with extinction (see Supporting Information S1). For each species present in EACs, we also retrieved the corresponding numbers of individuals imported in Japan since 1975 and their source (i. e. , captive-bred; wild-caught; ranched and farmed, see Supporting Information S1 for more details on the terminology). To assess the invasion potential of species present in EACs, we compared the recorded species to the lists of invasive and potential invasive species of Japan (National Institute for Environmental Studies, 2019). Data management and descriptive analyses were conducted using R (version 3. 6. 0; R Core Team, 2017).
EACs might play an overlooked role in the observed trends of wild animals legally and illegally imported in Japan. International trade in exotic pets is an important and increasing driver of biodiversity loss for many species  (Sodhi et al. , 2004). For instance, the ball python (Python regius) is the most common live species imported in Japan and was one of the most encountered snake species in EACs. According to the CITES trade database, a large portion (>46%) of ball pythons imported in Japan between 2010 and 2019 was not bred in captivity. The intensive trade of ball pythons is substantially impacting wild populations in their natural range  (Auliya et al. , 2016; Harrington et al. , 2020). We found that most specimens imported in Japan in 2010-2019, belonging to species present in EACs and CITES-listed, were imported under the "captive-bred" term (e.
For many species with well-established trade, there is a lack of fundamental knowledge on their biology and on the impacts of harvesting on populations  (Auliya et al. , 2016; Janssen & Gomez, 2021). This highlights the importance of a precautionary approach to identify and decide on sustainable exploitation practices  (Smith et al. , 2011). Seven CITES-listed species had no matching record of importations over the past 45 years in Japan. This could be evidence of a local trade of species coming from breeding facilities in Japan or from zoos. For instance, some Japanese zoos breed otters, later selling them for the pet trade and/or purchasing them from animal dealers  (Kitade & Naruse, 2018). Several rare primate species such as the vulnerable Sunda slow loris, the Senegal bushbaby (Galago senegalensis), and the white-faced saki (Pithecia pithecia), with no recent records in the CITES trade database, are also displayed in EACs.
I G U R E 2 (a,b) Temporal trends of Exotic Animal Cafés (EACs, n = 142): (a) number of active EACs; and (b) number of EAC openings (in red: owl cafés; and in blue: every other type of EACs) with live imports of owls (for the two most common families: Strigidae and Tytonidae) for trade in Japan between 2010 and 2019. Opening information was obtained from the website or social media accounts (Facebook, Instagram, and/or Twitter) of EACs with their first online activity as an indicator. Number of owls imported in Japan during that period comes from the Convention on International Trade in Endangered Species of Wild Fauna and Flora database F I G U R E 3 Number of exotic animal cafés (EACs) displaying at least one individual belonging to a threatened species of birds or reptiles. Most exotic animal species surveyed in EACs and listed by the International Union for Conservation of Nature (IUCN) Red List as threatened (CR, critically endangered; EN, endangered; VU, vulnerable) or near threatened (NT) belonged to the Aves (28 species, 309 individuals) and Reptilia (41 species, 165 individuals) classes (see Tables S1 and S2 for details) reevesii (n = 34,126, >99% captive bred), and 31 species with over 50% of individuals being "wild-caught," "ranched," or "farmed" such as Southeast Asian water monitor Varanus salvator (n = 11,763, >95% wildcaught), Nile monitor Varanus niloticus (n = 4755, >80% ranched) or Diamondback terrapin Malaclemys terrapin (n = 2089, >86% farmed).
ACKNOWLEDGMENTS Marie Sigaud  and  Cécile Sarabian  were supported by the  Japan Society for the Promotion of Science. We thank  Conserv'Session  (http://www. wildlife-science. org/en/ConservSession/) for being the starting point of this project and  Prof. Andrew MacIntosh  for his comments on an earlier version of the article that greatly contributed to its improvement. We also thank  Dr. Aude Bourgeois ,  Yayasan International Animal Rescue Indonesia  and the  Vétérinaires de la Faune Sauvage group  for their help in species identification. ORCID Marie Sigaud https://orcid. org/ 0000-0002-6958-7239.
F I G U R E 2 (a,b) Temporal trends of Exotic Animal Cafés (EACs, n = 142): (a) number of active EACs; and (b) number of EAC openings (in red: owl cafés; and in blue: every other type of EACs) with live imports of owls (for the two most common families: Strigidae and Tytonidae) for trade in Japan between 2010 and 2019. Opening information was obtained from the website or social media accounts (Facebook, Instagram, and/or Twitter) of EACs with their first online activity as an indicator. Number of owls imported in Japan during that period comes from the Convention on International Trade in Endangered Species of Wild Fauna and Flora database F I G U R E 3 Number of exotic animal cafés (EACs) displaying at least one individual belonging to a threatened species of birds or reptiles. Most exotic animal species surveyed in EACs and listed by the International Union for Conservation of Nature (IUCN) Red List as threatened (CR, critically endangered; EN, endangered; VU, vulnerable) or near threatened (NT) belonged to the Aves (28 species, 309 individuals) and Reptilia (41 species, 165 individuals) classes (see Tables S1 and S2 for details) reevesii (n = 34,126, >99% captive bred), and 31 species with over 50% of individuals being "wild-caught," "ranched," or "farmed" such as Southeast Asian water monitor Varanus salvator (n = 11,763, >95% wildcaught), Nile monitor Varanus niloticus (n = 4755, >80% ranched) or Diamondback terrapin Malaclemys terrapin (n = 2089, >86% farmed).
A corrigendum on A MALDI-TOF MS library for rapid identification of human commensal gut bacteria from the class Clostridia by Asare, P. T. , Lee, C. -H. , Hürlimann, V. , Teo, Y. , Cuénod, A. , Akduman, N. , Gekeler, C. , Afrizal, A. , Corthesy, M. , Kohout, C. , Thomas, V. , de Wouters, T. , Greub, G. , Clavel, T. , Pamer, E. G. , Egli, A. , Maier, L. , and Vonaesch, P. ( ). Front. Microbiol. :. doi:. /fmicb. In the original article, there was an error in the Data availability statement. The dataset deposited on the Zenodo link was incomplete and the MALDI spectra were named with the old taxonomy. We have now corrected these errors, added a few strains and taken out two strains with ambiguous spectra. We have uploaded the ClostriTof version 2. 0 database plugin on Zenodo. The correct Data availability statement appears below.
"The datasets presented in this study can be found on Zenodo under the following link: https://zenodo. org/record/7773644#. ZCny2y8Rrfc. " In the published article, there was an imprecision in the Conflict of interest. This sentence previously stated: "The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. " The correct Conflict of interest appears below.
Acknowledgements We acknowledge the  World Climate Research Programme  for coordinating and promoting CMIP and all participants of CMIP6 and GeoMIP. We thank the  Super Computing Center of Beijing Normal University  for providing computing resources. We thank  Dr. Burke  for providing the computer code calculating permafrost probability with the observation-based relationship. This study was supported by  National Nature Science Foundation of China  (No. 41875126 ). Andy Jones  was supported by the  555 Met Office Hadley Centre Climate Programme  funded by  BEIS. Ulrike Niemeier  has been supported by the  Deutsche Forschungsgemeinschaft Research Unit VollImpact  ( FOR2820  (grant no. 398006378 )) and used resources of the Deutsches https://doi. org/10. 5194/esd-2022-34 Preprint. Discussion started: 22 July 2022 c Author(s) 2022.
Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at 55 a greater level of disaggregation in terms of land categories (i. e. , forest land, deforestation, organic soils, other land uses) and countries. Our results confirm a large difference in land use CO2 fluxes between the ensemble mean of the BMs, estimating a source of 4. 3 GtCO2 yr -1 globally for the period 2001-2020, and NGHGIs, which estimate a sink of -1. 7 GtCO2 yr -1. Most of this 6. 0 GtCO2 yr -1 gap is found on forest land (3. 8 GtCO2 yr -1 ), with differences also for 60 deforestation (1. 1 GtCO2 yr -1 ), other land uses (1. 0 GtCO2 yr -1 ), and to a lesser extent for organic soils (0. 1 GtCO2 yr -1 ). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-5.
200 this obligation differs for Annex I Parties (AI, advanced economies with annual GHG reporting commitments under the UNFCCC) and non-Annex I Parties (NAI, countries with less stringent reporting commitments), but a more harmonized reporting under the Paris Agreement is expected through Biennial Transparency Reports starting by the end of 2024. In this study, we use the most up-to-date and complete compilation of LULUCF CO2 data based on country 205 submissions to UNFCCC  (Grassi et al. , 2022) , openly available at https://doi. org/10. 5281/zenodo. 6390739. This database builds on a detailed analysis of a range of country reports to the UNFCCC, which include anthropogenic emissions and removals estimated following the IPCC methodology  (IPCC, 1996 (IPCC, , 2006)). The CO2 flux database is complemented by information on managed and unmanaged forest areas as available in NGHGIs submitted to the UNFCCC.
Specifically, for AI countries, data are from annual GHG inventories. For 210 NAI countries, the most recent and complete information was compiled from different sources, including National Communications, Biennial Update Reports, submissions to the framework REDD+ (Reducing Emissions from Deforestation and Forest Degradation), and NDCs. The data are disaggregated into fluxes from forest land, deforestation, organic soils, and other sources (Table  1 ). This database includes LULUCF data from 185 countries, covering 99. 9% of the global forest (i. e. , the land use category for which countries report the vast 215 majority of the emissions and removals). To ensure a complete time series from 2001 to 2020, which is often not yet available in NAI countries, gaps were filled without altering the levels and trends of the country-reported data.
Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps and areas of improvement still remain. Yet, these gaps are expected to be progressively filled under the Paris Agreement Transparency Framework reporting. Most 220 NAI countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks or inconsistent estimates among different reports, and several report incomplete estimates (especially in Africa, where many countries still have low national capacity for reporting). For more details, including a discussion of the differences between the NGHGI database, the UNFCCC GHG data interface (UNFCCC, 2022b), and the FAOSTAT Land use emission database  (Tubiello et al.
0 GtCO2 yr -1 emerges from our study (Figure  3a ). Most of the difference between BMs and NGHGIs is found on forest land (3. 8 GtCO2 yr -1 , Fig. 3b ), but discrepancies emerge also for deforestation (1. 1 GtCO2 yr -1 , Fig. 3c ) and other fluxes (1. 0 GtCO2 yr -1 , Fig. 3e ). By contrast, the 340 difference for organic soils, which are added to the BM estimates from external datasets (see Table  1 ), is small (0. 1 GtCO2 yr -1 , Fig. 3d ). In general, trends are quite similar between BMs and NGHGIs. Forest land and deforestation are the categories where the spread among BMs is greatest, with H&N showing smaller absolute values of forest sink and emissions from deforestation than OSCAR, while BLUE has intermediate values. The differences are largely the result of different rates of deforestation and different 345 estimates of carbon densities for the forests affected, and partly due to different allocation of emissions associated with shifting agriculture.
-Figure 3- The LULUCF flux gap between BMs and NGHGIs identified here is similar to the 5. 5 GtCO2 yr -1 previously found between Integrated Assessment Models (IAMs, whose approach to estimating the anthropogenic land-350 use CO2 flux is similar to BMs) and NGHGIs  (Grassi et al. , 2021). However, the updates presented here shifted the values downwards, i. e. smaller net emissions in BMs (due to updates in the land use dataset used as input for BM simulations, see  Friedlingstein et al. 2022) , and a greater net sink in NGHGIs (due to more complete reporting by NAI countries,  Grassi et al. , 2022) , and made the trends more similar (Figure  4 ).
2021). For the same period, the adjustment estimated 375 here is -5. 3 GtCO2 yr -1. The similarity confirms that the adjustment applied by  Grassi et al. 2021  to future IAM emission pathways, even if derived only from a single model, was well representative of the ensemble mean. In both cases, the adjustments reconcile most of the gaps identified between the anthropogenic land-use CO2 flux estimated by NGHGIs and by global models (either IAMs or BMs). The remaining gaps -i. e. , about -0. 5 GtCO2 yr -1 in  Grassi et al. (2021)  for 2005-2020, and -0. 9 GtCO2 yr -1 in this study for 2000-2020 -are well within the 380 uncertainty of the respective datasets (e. g. see the large variability of DGVM estimates in Figure  5b ).
This consistency arising from using two independent approaches provides confidence on the estimated size of Figure  10a  plus the striped managed forest area in Figure  10b ) compare well with the NGHGIs (Figure  10e ), with both datasets indicating a relatively small net sink in managed land globally. This sink results from a large net sink in temperate and boreal regions (mostly represented by AI countries) and a small net source in the 490 tropics (mostly represented by NAI countries, Figure  7a ). However, a few lines of reasoning and evidence suggest a possible underestimation of the net global sink in managed land, in both the adjusted BM results and the NGHGIs. First, the fact that 80% of land is under some form of management  (Erb et al. 2017 ) could suggest, as a first approximation, that a similar share of the total (forest and non-forest) natural terrestrial sink due to indirect 495 effects (Figure  10b ) -i.
Consequently, a corresponding underestimation of the natural sink in forest areas is implied (i. e. , the sum of 535 dashed and dark green areas in Figure  10b  should be bigger and the gray area be smaller) to maintain consistency between the natural terrestrial sink derived from DGVMs and the natural terrestrial sink derived as residual sink, which is constrained by the other budget components from the Global Carbon Budget  (Friedlingstein et al. 2022 ). Lastly, it is possible that NGHGIs underestimate the overall net sink in managed lands. While a few NGHGIs 540 likely underestimate emissions or overestimate removals of CO2, these effects are at least partly counterbalanced by the incomplete reporting in terms of land uses (including about 0. 3 Billion ha of forest with no estimates of carbon flux, and very scarce data for non-forest land in developing countries,  Grassi et al.
Way forward: how to operationalize comparisons between global models and NGHGIs? This study focused on the main conceptual difference between BMs and NGHGIs. Our approach reconciles most of the current large gap in land use CO2 fluxes between the two datasets, and provides a greater level of 550 spatial and process details than previous analyses. Our adjustment of BM results should be seen as a short-term and pragmatic fix based on existing data, rather than a definitive solution to bridge the difference between global models and NGHGIs. Additional steps are needed -from both global models and NGHGIs -to understand and reconcile the remaining differences, some of which are relevant at the country level, and to operationalize future comparisons between global models and 555 NGHGIs. From the global models' side, other studies have already highlighted many fundamental challenges  (Pongratz et al 2021) , including the need for a better representation of land management processes and forest demography.