dataesr/scientific-paragraphs-categorization huggingface dataset:
Surface fluxes and prescribed sources/sinks The emissions and surface fluxes provide the surface boundary conditions for the atmospheric concentrations of CO2 and CH4. They play a crucial role in determining the variability and growth rate of both greenhouse gases in the atmosphere. Errors in the budget of the total flux will result into systematic errors or biases in forecast of atmospheric CO2 and CH4. In the CAMS reanalysis the surface fluxes (including sources and sinks) are not optimized by the assimilation system. This lack of surface flux optimization can lead to biases in the analysis when the observing system coverage is sparse in space and time or when the observation error is large, and the analysis is strongly influenced by the model forecast. Table  2  lists the datasets used to produce the CAMS reanalysis.
They include: • Fire emissions derived using the CAMS Global Fire Assimilation System (GFAS) version 1. 2 that assimilate fire radiative power observations from satellite-based sensors  (Kaiser et al. , 2012). GFAS produces daily estimates of wildfire and biomass burning emissions. The emissions are injected at the surface and distributed over the boundary layer by the model's convection and vertical diffusion scheme. • Biogenic CO2 fluxes are based on the online CHTESSEL module  (Boussetta et al. , 2013)  that relates CO2 biogenic fluxes with radiation, precipitation, temperature, humidity, and soil moisture. CHTESSEL is used in conjunction with the biogenic flux adjustment system (BFAS) that improves the continental budget of CO2 fluxes by combining information from fluxes estimates by a global flux inversion system  (Chevallier et al.
6(d)). Note that the switch to the near-real time retrievals for IASI-B XCH4 has a much more marginal impact on the system (Fig. 6 (c)). Validation against a set of independent observations has been performed on the 18 years of the CAMS GHG reanalysis span. The independent data includes different types of observations: in situ near-surface continuous observations of CO2 and CH4 mole fractions from the collaborative ObsPack datasets  (Schuldt et al. , 2020; Sarra et al. , 2021 ; NOAA Carbon Cycle Group ObsPack Team, 2019; see Table  A1 ); dry-air column-averaged mole fractions from the Total Carbon Observing Network (TCCON,  Wunch et al. 2011 Wunch et al. , 2015)) ; tropospheric and stratospheric partial columns for CH4 from the Network for the Detection of Atmospheric Composition Change (NDACC, De Mazière et al.
Carbon dioxide Overall, the error is within 10 ppm and 4 ppm for most of the near-surface and total column stations respectively for the whole 18-year period (Figs  7  and 8  which is consistent with the XCO2 error at the TCCON sites (Fig. 8 ). This positive bias decreases from 2007 to 2015 when IASI-A CO2 data are assimilated, with values lower than 2 ppm, and becomes negative from 2015 to 2019 (from 0 to -2 ppm). From 2019 onwards, there is a positive trend in the bias, and it becomes positive (> +2 ppm) in 2020. There is consistency between the column and surface biases with a general positive bias at background stations before 2015 and a negative bias after 2015 (up to 2019) at the surface stations, although there is no data in 2020 from the surface stations.
Vertical profiles The uncertainty of CAMS GHG reanalysis varies with height and the accuracy of the analysis vertical profiles depends mostly on the underlying model uncertainty, as the satellite data assimilated in reanalysis only provide integrated total or partial atmospheric column. The reanalysis has been evaluated using observations of CO2 and CH4 vertical profiles  (Karion et al. , 2010; Baier et al. , 2021)  from the NOAA AirCore dataset v20210813. It includes 133 vertical profiles from the surface to the lower stratosphere (up to around 40 hPa) from 2012 to 2020 at 7 sites listed in Table A. 3. Figure  12  shows that the largest mean error occurs (i) near the surface with a strong influence from surface fluxes; (ii) in Upper Troposphere/Lower Stratosphere (UTLS) region (between 500 hPa and 100 hPa) with a strong influence from longrange transport; and (iii) in the stratosphere (above 100 hPa) where uncertainties associated with chemical loss of CH4 and the meteorology driving the tracer transport are largest, and the fact that satellite data used here are not able to constrain the stratospheric CO2 and CH4 in the reanalysis.
Trends Although this reanalysis is using a consistent underlying model and re-processed observations of CO2 and CH4, the current system is not able to provide accurate enough atmospheric mole fraction that can be used to estimate trends and the atmospheric growth rate as computed by the changes in global mean CO2 and CH4 from one year to the next. The CO2 and CH4 global annual means based on Marine Boundary Layer (MBL) reference sites are compared to the NOAA Global Greenhouse Gas Reference Network (GGGRN) observations (https://gml. noaa. gov/ccgg/about. html, Andrews et al. , 2014; Conway et al. , 1994; Dlugokencky et al. , 1994)  in Fig. 14. Changes in the assimilated satellite data have a clear impact on the evolution of the global annual mean values of CO2 and CH4 in the CAMS GHG reanalysis.
Limitations and caveats This section provides an overview of the shortcomings of the CAMS GHG reanalysis which users should consider when interpreting the data. The main issues documented in the previous sections are summarised below: 1. Emissions are prescribed and not adjusted by the data assimilation system in the CAMS reanalysis (Sect. 2. 3). This leads to a growing model error for CO2 and CH4 that can be difficult to correct with a sparse observing system and 12-hour assimilation window. In addition, the prescribed emissions are not available in near-real time, which means they are either kept fixed since the last year available (e. g. 2010 for CH4) or they are extrapolated with a climatological trend as done for CO2 (see details in Sect. 2. 3). Because of this, the CAMS GHG reanalysis is not suitable to investigate the impact of local emission changes, such as COVID impact studies, which require a large local emission adjustments to the prescribed inventories (e.
2. Changes of satellite data used with different temporal, horizontal and vertical coverage cause changes in the quality of the reanalysis. For example, winter seasons have a lower number of observations because of light conditions and the higher frequency of cloud presence. This affects the quality of the seasonal cycle and the inter-hemispheric gradient. Similarly, in regions where there is no observation coverage, such as the stratosphere, the reanalysis is based on the underlying model including its systematic errors (see discussion on stratospheric biases in Sect 3. 2). 3. Changes in satellite retrievals affect the quality of the observations used in the CAMS GHG reanalysis. For example, the switch from the CCI re-processed satellite products to the near-real time products is associated with a marked change in the bias and random error (i.
Code and data availability The IFS forecasting and reanalysis system is not for public use as the ECMWF Member States are the proprietary owners. The resulting dataset is however freely available on the Copernicus Atmosphere Data Store. The CAMS GHG reanalysis can be accessed through the CAMS Atmosphere Data Store (ADS) at https://doi. org/10. 24380/8fck-9w87. The format is available in both GRIB and NetCDF. The data record starts on 1 January 2003 00UTC and currently stops on 31 December 2020. Recent months will be added over time as soon as the reanalysis procedure and its validation are completed. The original data was available either as spectral coefficients with a triangular truncation of T255 or on a reduced Gaussian grid with a resolution of N128. But for the ease of the user, fields were interpolated from their native representation to a regular 0.
at https://cds. climate. copernicus. eu/cdsapp#!/dataset/satellite-https://doi. org/10. 5194/egusphere-2022-283 Preprint. Discussion started: 18 July 2022 c Author(s) 2022. CC BY 4. 0 License. carbon-dioxide for CO2 and https://cds. climate. copernicus. eu/cdsapp#!/dataset/satellite-methane for CH4. The GHG reanalysis integrate the L2 GHG data from the following satellite instruments: • SCIAMACHY -Envisat: The The SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument onboard the Envisat satellite was launched by the European Space Agency (ESA) in March 2002 and it was developed by a consortium involving the Netherland Space Office, the German Aerospace Centre and the Belgian Federal Science Policy Office. It measures radiances variations from the ultraviolet to the near visible infrared.
Anthropogenic emissions from the Emission Database for Global Atmospheric Research (EDGAR) version 4. 2FT2010 inventory (Janssens-Maenhout et al. , 2011; Olivier and Janssens-Maenhout, 2012) excluding the short carbon cycle. The anthropogenic emissions are based on annual average values and include fossil fuel combustion and leakage, agricultural, landfill/waste emissions and aviation (based on the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP, Lamarque et al. , 2013) nitric oxide (NO) emissions from aviation scaled to the annual CO2 total emission from aviation from EDGAR). Anthropogenic emissions of CO2 are extrapolated from 2010 to 2014 with the time series of country totals from EDGARv4. 3 (Janssens-Maenhout et al. , 2016) and from 2015 to 2020, a persistent growth based on the last available year (2014) is applied.
This overestimation is https://doi. org/10. 5194/egusphere-2022-283 Preprint. Discussion started: 18 July 2022 c Author(s) 2022. CC BY 4. 0 License. consistent with a drift in the IASI CO2 data towards a growing negative bias. After 2018, part of the drift is due to the fact that IASI (version v4. 0) is saturating with increasing atmospheric CO2. Note that this has been corrected with v9. 1 (available on the C3S datastore). A sudden change in the IASI-B XCO2 departures is visible in Fig. 5(c) around December 2018, in correspondence of the switch from the ESA-CCI reprocessed dataset to a near-real time LMD dataset used operationally in the CAMS GHG analysis. The transition to a new dataset was made necessary as the reanalysis production was running close to real-time and reprocessed observations were not available.
After the transition to near-real time observations, the IASI XCO2 increments are reduced to almost zero, as hinted by the overlap between the red (AN departure) and green line (BG departure) in Fig. 5(c). At the same time, a drop in the number of assimilated IASI XCO2 observations is observed (blue line, same panel and figure). Together with a drastic reduction in the magnitude of the increments, a large negative bias of approximately 5ppm in both the AN and BG departures emerges, accompanied by a large standard deviation error (~20 ppm, cf. dashed lines in same panel and figure). This degradation in the quality of the IASI-B XCO2 observations in the near-real time dataset is due to the change of the correction of the non-linearity of the detector of IASI-B that was made by CNES and EUMETSAT onAugust 17 th 2018 and that introduced a bias of ~0.
2 K on the channels used to perform the CO2 retrieval. This change has been corrected in the versions of IASI-B MT-CO2 that are available on the C3S datastore but were not used for this reanalysis. In January 2019, there was also a transition from the ESA-CCI GOSAT XCO2 retrievals to the near-real time IUP-UB retrieval product (Heymann et al. , 2015; Massart et al. 2016)  as shown inFig. 5(d). Consequently, the standard deviation of both the AN and the BG departures increases (cf. dashed lines, same panel and figure), suggesting that the near-real time data is noisier than the reprocessed dataset from ESA-CCI.
(a)). The standard deviation of both the AN and BG departures are smaller for GOSAT (around 10 ppb, dashed lines in Fig. 6(d)) than for SCIAMACHY (around 20 ppb, dashed lines in Fig. 6(a)), indicating that GOSAT provides less noisy observations. Similar to what was observed for CO2, a discontinuity in the mean AN and BG departures of GOSAT XCH4 emerges in January 2019, in correspondence of the transition from the ESA-CCI dataset and the NRT SRON retrievals (see dashed pink line in Fig. 6(d)). Both the AN and the BG departures change sign,.
https://doi. org/10. 5194/egusphere-2022-283 Preprint. Discussion started: 18 July 2022 c Author(s) 2022. CC BY 4. 0 License. satellite data and the resulting CAMS GHG reanalysis. For this reason, a new re-run of the CAMS GHG reanalysis from 2019 onwards will be performed with consistent C3S satellite products in the near future. The comparison of global mean values with observations shows variations in the bias that depend on changes in the assimilated satellite data of around 2 ppm and 10 ppb for CO2 and CH4 respectively, which have the same magnitude as the observed variations in their growth rate. For this reason, we do not recommend the use of this dataset to study changes in the atmospheric growth rate of CO2 and CH4. Similarly, large biases in stratospheric CO2 and CH4 should also considered when analysing stratospheric signals and trends in the CAMS GHG reanalyses.
Acknowledgements The Copernicus Atmosphere Monitoring Service is operated by the  European Centre for Medium-Range Weather Forecasts  on behalf of the  European Commission  as part of the Copernicus Programme (http://copernicus. eu). The satellite data assimilated in the CAMS GHG reanalysis were obtained from the  ESA GHG-CCI  project (https://climate. esa. int/en/projects/ghgs/Data) and the  Copernicus Climate Change Service  ( C3S ) Climate Data Store (https://cds. climate. copernicus. eu). The data used to evaluate the CAMS GHG reanalysis were obtained from: the  Total Carbon Column Observing Network  (TCCON) Data Archive hosted by CaltechDATA at https://tccondata. org. ;  CH4  from the  Network for the Detection of Atmospheric Composition Change  (NDACC, www. ndacc. org;Sussmann et al.
, 2011;2013; De Mazière  et al. , 2018) (see Table  A2 ); the  Observation Package (ObsPack)  GLOBALVIEWplus Data Products https://gml. noaa. gov/ccgg/obspack; the  NOAA AirCore  dataset https://gml. noaa. gov/ccgg/arc/?id=144; and the  NOAA Greenhouse Gas Marine Boundary Layer Reference  https://gml. noaa. gov/ccgg/mbl/mbl. html. For the NDACC data, the  National Center for Atmospheric Research  is sponsored by the  National Science Foundation. The NCAR FTS observation programs at Thule, GR, Boulder, CO and  Mauna Loa , HI are supported under contract by the  National Aeronautics and Space Administration (NASA). The Thule work is also supported by the  NSF Office of Polar Programs (OPP). We wish to thank the  Danish Meteorological Institute  for support at the Thule site and  NOAA  for support of the MLO site.
• Anthropogenic emissions from the Emission Database for Global Atmospheric Research (EDGAR) version 4. 2FT2010 inventory (Janssens-Maenhout et al. , 2011; Olivier and Janssens-Maenhout, 2012) excluding the short carbon cycle. The anthropogenic emissions are based on annual average values and include fossil fuel combustion and leakage, agricultural, landfill/waste emissions and aviation (based on the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP, Lamarque et al. , 2013) nitric oxide (NO) emissions from aviation scaled to the annual CO2 total emission from aviation from EDGAR). Anthropogenic emissions of CO2 are extrapolated from 2010 to 2014 with the time series of country totals from EDGARv4. 3 (Janssens-Maenhout et al. , 2016) and from 2015 to 2020, a persistent growth based on the last available year (2014) is applied.
g. , Chainlink Data Feeds  [13, 17]. In the case of the two largest lending platforms, AAVE V2  [2]  and Compound  [21] , for instance, Chainlink provides the price of each asset in ETH and USD, respectively. We found 16,418 liquidations in AAVE and 6387 liquidations in Compound. Out of these, there were 4863 AAVE liquidations and 2036 Compound liquidations that were sent privately through Flashbots. In AAVE, the three largest collateral assets that were liquidated were WETH (57. 58%), LINK (11. 84%), and WBTC (8. 99%). The debt assets paid for, i. e. , the assets borrowed by the users, were USDC (33. 77%), USDT (22. 27%), DAI (19. 39%), and GUSD (5. 12%), all of which are stablecoins and account for over 80% of the assets repaid by liquidators. In Compound, the three largest collateral assets that were liquidated were WETH (69.
From our dataset, we selected those with a SPPE greater than or equal to 1% (12,983,282 transactions in total) and checked if they were said to be accelerated by BTC. com's API. Of these transactions, 14,104 were found to have been accelerated. Our findings also show that transaction acceleration services are becoming quite common among Bitcoin mining pools (as shown in Figure  11 ). Between 2018 and April 2019, only BTC. com and F2Pool alone accounted for most of the accelerated transactions. However, as of December 2020, we see that BTC. com accounts for a very small fraction of accelerated transactions, with AntPool, Huobi, and F2Pool accounting for most of the accelerated transactions.
Acknowledgments This research was supported in part by a  European Research Council (ERC)   Advanced Grant  " Foundations for Fair Social Computing ", funded under the  European Union 's  Horizon 2020 Framework Programme  (grant agreement no. 789373 ). It was also supported by  MIAI @ Grenoble Alpes  ( ANR-19-P3IA-0003 ) and by the  French National Research Agency  under grant  ANR-20-CE23-0007.
Acknowledgements The work of  Xiaohu Wu  has been partially supported by the  National Key R&D Program of China  ( 2022YFB2902900 ). The work of  Patrick Loiseau  has been partially supported by  MIAI@Grenoble Alpes  ( ANR-19-P3IA-0003 ), by the  French National Research Agency (ANR)  through grant  ANR-20-CE23-0007  and through the  "Investissements d'avenir" program  ( ANR-15-IDEX-02 ); and by the  Alexander von Humboldt Foundation.
Results A total of n = 4,245 professionals in psychiatry completed the survey. The majority of the participants were female (n = 2,856, 67%), while young professionals between the ages of 24 and 35 years (n = 1856, 44%) were overrepresented in the sample. Most of them worked in adult psychiatry (n = 3,320, 78%) and inpatient facilities (n = 1884, 44%). Working facility of the participants and their years of experience in psychiatry are attached as a Supplementary material S1. There were no missing values in the dataset. First, based on the suitable Bartlett's Test of Sphericity (p < 0. 0001) and the KMO measure of sampling adequacy (0. 841) we performed an EFA on the pooled sample to see the factor matrix, the network of the items, and to create an overview of the structure of the scale. To determine the number of factors for extraction, parallel analysis was performed, and the eigenvalues were considered, which identified 3 or 4 different dimensions.
Acknowledgments We are sincerely thankful to  Roshelle Ramkisson  (consultant psychiatrist at  Pennine Care NHS Foundation Trust, United Kingdom ) and  Cleo L. Crunelle  (professor at  Vrije Universiteit Brussel, Department of Psychiatry, Belgium ) who provided supervision for the project in the  United Kingdom and in Belgium. We express our gratitude to  Anne Destrebecq  (professor at  Department of Biomedical Sciences for Health, University of Milan, Italy ) and to  Paolo Ferrara  (tutor and lecturer at  San Paolo Bachelor School of Nursing, San Paolo Teaching Hospital ,  University of Milan, Italy ) for letting us use their Italian translation of the  OMS-HC. Also, thanking to  Anna Sofie Hansen  (child psychiatrist at  Aalborg University Hospital, Research Unit for Child and Adolescent Psychiatry, Aalborg, Denmark ) for the preparation of the Danish data collection and Danish translation of the questionnaire.
Antimicrobial resistance (AMR) is a public health threat responsible for 700,000 deaths per year worldwide. Antimicrobial use (AMU) in livestock contributes to AMR in animal and public health. Therefore, it is essential to implement effective interventions towards better AMU in livestock. However, there is a lack of evidence to inform decision-makers of what works, how, for whom and why and how effective interventions can be adapted to different contexts. We conducted a scoping review and an impact pathway analysis to systematically map the research done in this area and to inform evidence-based and context-appropriate policies. We followed the PRISMA-ScR requirements and searched Web of Science, PubMed and Scopus databases to identify studies in English or French languages, in open access and published between 2000 and 2022.
Identification of relevant studies We searched Web of Science, PubMed and Scopus databases estimating they cover the entire literature on the AMU issue. We limited the search to all types of studies in English and French languages, in open access and published between January 1st, 2000 and July 26th, 2022. Most studies were published post-2000, so we anticipated the effect of the time limitation to be minimal. Within the same PICo component, the search terms were linked by the Boolean connector "OR" and the three resulting search strings were linked by the Boolean connector "AND". The final search strategy for Scopus can be found in the Supplementary material (S1 List). Additional studies identified through an exploratory literature review on the same topic were included in the selection process 4.
Study selection The studies were selected through two screening phases: 1) a first screening applied to the titles and abstracts; 2) a second screening applied to the full texts. Nine exclusion criteria were used to select references at each phase (EC 1 to EC 9 in Table  2 ). An additional exclusion criterion (EC 10 in Table  2 ) related to the lack of rigor or relevance according to the objective of the review was applied during the second screening phase. The aim was to determine whether the study provided relevant evidence and whether the conclusions drawn by the researchers made a sufficiently credible contribution to the understanding of interventions' ToC. Exclusion criteria were tested on forty references and validated by all authors. The study selection process was performed on the free, open-source reference management Zotero software and a Microsoft Excel database.
Data charting process Data extraction of the eligible references was performed by one author on the Nvivo qualitative data analysis software. Variables were previously validated by all authors. Each reference was identified as a case study and categorized according to a file classification including data on reference characteristics (e. g. , author name, year of publication, title, scientific journal, type of reference, electronic database, language, type of study, sub-type of study, type of analysis, study period), and a case classification including data on intervention characteristics (e. g. , name of the intervention, year of implementation, country, world region, type of production system, animal species, type of governance approach, specificity of the intervention). To answer our research questions (What works? How? For whom? And why?) and to understand the underpinning ToC of successful interventions in livestock, we performed a thematic content analysis of the references to identify codes and sub-codes  (Vaismoradi et al.
Selection of sources of evidence After duplicates were removed and additional references included, a total of 1786 references were identified from searches of electronic databases. Based on titles and abstracts, 1472 were excluded, with full-text references to be retrieved and assessed for eligibility. Of these, 284 were excluded for the exclusion criteria. The study selection process is presented in the Supplementary material (S3 Table ). The remaining references were considered eligible for this review (Fig. 1 ).