dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by  EPSRC  Grant  EF/M027740/1  (which funded M. C. H. -G. work) and the  Leverhulme Trust  Grant  RPG-2016-136  (which funded R. H. A. -S. , C. P. and S. A. work). We thank  Prof. Peter Hine ,  Prof. Richard Bushby  and  Dr. Richard Blackburn  for help with the analysis of the data and comments on the manuscript. M. E. R. is grateful to the  Royal Society  for funding his Industrial Fellowship. Y. H. has been supported by the  Finnish Centre of Excellence in Molecular Biology of Primary Producers  ( Academy of Finland CoE program   2014-2019 ) decision # 271832. Y. H. laboratory was funded by the  Gatsby Foundation  [ GAT3395/PR3 )]; the  National Science Foundation Biotechnology and Biological Sciences Research Council  grant  [BB/ N013158/1 ];  University of Helsinki  [award 799992091], the  European Research Council   Advanced Investigator Grant SYMDEV  [No.
Paper Filtering Process An early list of publications suitable for inclusion in the review has been selected from: -Bibliographic databases (using the previously described search strings): Scopus, Web of Science, Agris, Medline, CAB; -References quoted in the project proposal; -Grey literature (EU projects) quoted in the project proposal. Each retrieved reference entered the screening process, consisting of three steps: (1) Eligibility screening (Appendix B): The initial eligibility of studies was determined looking at easily identifiable features such as year of publication, language or geographical coverage. Specifically, a paper was discarded if it was not written in English, French or Spanish, or if it was written before 2000. Books and book chapters were included. Only eligible studies of the present criteria were further processed.
Screening Process Results The search in bibliographic databases produced 1618 records, 239 of which were considered suitable for a first screening to examine the titles. This number was reduced to 137 records after the eligibility screening (Appendix B) and finally to 98 entries after the methodological and relevance screenings (Appendices C-E), including additional material suggested by partners. Exploring the change over time in the number of published papers per year (Figure  2 ) highlights a clearly growing trend, with a marked increase from 2012 onwards. This overall trend was maintained during the selection process and supports our decision to focus only on recent studies (from 2000 onwards). conformity of the content analysis between different reviewers (for further details on the inter-coderreliability see Appendix F, Tables  A1  and A2 ).
Screening Process Results The search in bibliographic databases produced 1618 records, 239 of which were considered suitable for a first screening to examine the titles. This number was reduced to 137 records after the eligibility screening (Appendix B) and finally to 98 entries after the methodological and relevance screenings (Appendices C, D, and E), including additional material suggested by partners. Exploring the change over time in the number of published papers per year (Figure  2 ) highlights a clearly growing trend, with a marked increase from 2012 onwards. This overall trend was maintained during the selection process and supports our decision to focus only on recent studies (from 2000 onwards). To get a first overview of the content of the reviewed studies, thirteen categories were defined prior to abstract selection, and each record was assigned to one or more topics.
SFSCs and AFNs are shaping a new geography of food, which has a correspondence in new retail geography. In this framework, the motivations for retailing local products range from the willingness to meet local demand, to matching lifestyle changes, to company diversification strategies. The retailing sector is also affected by some problematic issues such as access to and availability of local products both linked to seasonality and to the limited quantities of products supplied by small local enterprises. However, there is very little empirical data to quantify the importance of direct sales and AFNs in terms of sales value  [82]. Finally, in line with a more critical view  [41] , although FMs are defined as the quintessential local delivery systems, the component of embeddedness seems significantly permeated by both instrumentalism and "marketness".
6, SSP3xRCP6. 0, SSP5xRCP8. 6 -to explore a wide range of land-use change and climate change futures. This paper describes the rationale for scenario selection, the process of harmonizing input data for land use, based on the second phase of the Land Use Harmonization Project (LUH2), and climate, the biodiversity and ecosystem services models used, the core simulations carried out, the harmonization of the model output metrics, and the treatment of uncertainty. The results of this collaborative modeling project will support the ongoing global assessment of IPBES, strengthen ties between IPBES and the Intergovernmental Panel on Climate Change (IPCC) scenarios and modeling processes, advise the Convention on Biological Diversity (CBD) on its development of a post-2020 strategic plans and conservation goals, and inform the development of a new generation of nature-centred scenarios.
Scenario selection All the models included in BES-SIM used the same set of scenarios with particular combinations of SSPs and RCPs. In the selection of the scenarios, we applied the following criteria: (1) data on projections should be readily available, and (2) the total set should cover a broad range of landuse change and climate change projections. The first criterion entailed the selection of SSP-RCP combinations that are included in the ScenarioMIP protocol as part of CMIP6  (O'Neill et al. , 2016) , as harmonized data were available for these runs and they form the basis of the CMIP climate simulations. The second criterion implied a selection of scenarios with low and high degrees of climate change and different land-use scenarios within the ScenarioMIP set. Our final selection was SSP1 with RCP2.
Input data A consistent set of land-use and climate data was implemented across the models to the extent possible. All models in BES-SIM used the newly released Land Use Harmonization version 2 dataset (LUH2,  Hurtt et al. , 2018). For the models that require climate data, we selected the climate projections of the past, present, and future from CMIP5/ISIMIP2a  (McSweeney and Jones, 2016)  and its downscaled version from the WorldClim  (Fick and Hijmans, 2017) , as well as MAGICC 6. 0  (Meinshausen et al. , 2011a, b)  from the IMAGE model for GLOBIO models (Table  2 ). A complete list of input datasets and variables used by the models is documented in Table  S1  of the Supplement.
0 (SSP3xRCP7. 0); however, climate projections (i. e. , time series of precipitation and temperature) are currently not available for RCP7. 0. Therefore, we chose the closest RCP available, which was RCP6. 0, for the standalone use of climate projections, and chose SSP3xRCP6. 0 for the land-use projections from the LUH2. In this paper, we refer to this scenario as SSP3xRCP6. 0. The land-use projections from each of the IAMs were harmonized using the LUH2 methodology. LUH2 was de-veloped for CMIP6 and provides a global gridded land-use dataset comprising estimates of historical land-use change  and future projections (2015-2100), obtained by integrating and harmonizing land-use history with future projections of different IAMs  (Jungclaus et al. , 2017; Lawrence et al. , 2016; O'Neill et al.
For biodiversity and ecosystem services models that rely on discrete, high-resolution land-use data (i. e. , the GLOBIO model for terrestrial biodiversity and the InVEST model), the fractional LUH2 data were downscaled to discrete land-use grids (10 arcsec resolution; ∼ 300 m) with the land-use allocation routine of the GLOBIO4 model. To that end, urban, cropland, pasture, rangeland, and forestry areas from LUH2 were first aggregated across the LUH2 grid cells to the regional level of the IMAGE model, with forestry consisting of the wood harvest from forested cells and nonforested cells with primary vegetation. Next, the totals per region were allocated to 300 m cells with the GLOBIO4 land allocation routine, with specific suitability layers for urban, cropland, pasture, rangeland, and forestry areas.
Climate data GCMs are based on fundamental physical processes (e. g. , conservation of energy, mass, and momentum and their interaction with the climate system) and simulate climate patterns of temperature, precipitation, and extreme events on a large scale  (Frischknecht et al. , 2016). Some GCMs now incorporate elements of Earth's climate system (e. g. , atmospheric chemistry, soil and vegetation, land and sea ice, carbon cycle) in Earth system models (GCMs with an interactive carbon cycle), and have dynamically downscaled models with higher-resolution data in regional climate models (RCMs). A large number of climate datasets are available today from multiple GCMs, but not all GCMs provide projections for all RCPs. In BES-SIM, some models require continuous time-series data. In order to harmonize the climate data to be used across biodiversity and ecosystem services models, we chose the bias-corrected climate projections from CMIP5, which were also adopted by ISIMIP2a  (Hempel et al.
, 2013)  or their downscaled versions available from WorldClim  (Fick and Hijmans, 2017). Most analyses were carried out using a single GCM, the IPSL-CM5A-LR  (Dufresne et al. , 2013) , since it provides mid-range projections across the five GCMs (HadGEM2-ESGFDL-ESM2M, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) in ISIMIP2a  (Warszawski et al. , 2014). The ISIMIP2a output from the IPSL-CM5A-LR provides 12 climate variables on daily time steps from the pre-industrial period 1951 to 2099 at 0. 5 • resolution (Mc-Sweeney and Jones, 2016), of which only a subset was used in this exercise (Table  S1 ). The WorldClim downscaled dataset has 19 bioclimatic variables derived from monthly temperature and rainfall from 1960 to 1990 with multi-year averages for specific points in time (e. g. , 2050, 2070) up to 2070.
Six models in BES-SIM used the ISIMIP2a dataset and three models used the WorldClim dataset. An exception was made for the GLOBIO models, which used MAGICC 6. 0 climate data  (Meinshausen et al. , 2011a, b)  in the IMAGE model framework  (Stehfest et al. , 2014) , to which GLOBIO is tightly connected (Table  3 ). The variables used from the climate dataset in each model are listed in Table  S1.
In BES-SIM, four species-based models were included: AIM-biodiversity  (Ohashi et al. , 2018) , InSiGHTS  (Rondinini et al. , 2011; Visconti et al. , 2016) , MOL  (Jetz et al. , 2007; Merow et al. , 2013) , and BIOMOD2 (Appendix A, Table  S2 ). The first three models project individual species distributions across a large number of species by combining projections of climate impacts on species ranges with projections of land-use impacts on species ranges. AIMbiodiversity uses Global Biodiversity Information Facility (GBIF) species occurrence data on 9025 species across five taxonomic groups (amphibians, birds, mammals, plants, reptiles) to train statistical models for current land use and climate to project future species distributions. InSiGHTS uses species' presence records from regular sampling within species' ranges and pseudo-absence records from regular sampling outside of species' ranges on 2827 species of mammals.
MOL uses species land-cover preference information and species presence and absence predictions on 20 833 species of amphibians, birds, and mammals. In-SiGHTS and MOL rely on IUCN's range maps as a baseline, which are developed based on expert knowledge of the species habitat preferences and areas of non-occurrence  (Fourcade, 2016). Both models use a hierarchical approach with two steps: first, a statistical model trained on current species ranges is used to assess future climate suitability within species ranges; second, a model detailing associations between species and habitat types based on expert opinion is used to assess the impacts of land use in the climate-suitable portion of the species range. BIOMOD2 is an R modeling package that runs up to nine different algorithms (e. g. , random forests, logistic regression) of species distribution models using the same data and the same framework.
The habitat affinities of the cSAR-iDiv model are estimated from the PREDICTS dataset  (Hudson et al. , 2017 (Hudson et al. , , 2016) )  while the habitat affinities of cSAR-IIASA-ETH come from a previously published database of studies  (Chaudhary et al. , 2015). The cSAR-iDiv model considers 9853 species for one taxonomic group (birds) in two functional groups (forest species and non-forest species) while cSAR-IIASA-ETH considers a total of 1 911 583 species for five taxonomic groups (amphibians, birds, mammals, plants, reptiles) by ecoregions (these are, however, not 1 911 583 unique species as a species present in two ecoregions will be counted twice). BILBI couples application of the species-area relationship with correlative statistical modeling of continuous spatial turnover patterns in the species composition of communities as a function of environmental variation.
Through space-for-time projection of compositional turnover (i. e. , change in species), this coupled model enables the effects of both climate change and habitat modification to be considered in estimating the proportion of species persisting for 254 145 vascular plant species globally. Three community-based models -PREDICTS, GLOBIO Aquatic  (Alkemade et al. , 2009; Janse et al. , 2015) , and GLOBIO Terrestrial  (Alkemade et al. , 2009; Schipper et al. , 2016)  -estimate a range of assemblage-level metrics based on empirical dose-response relationships between pressure variables (e. g. , land-use change and climate change) and biodiversity variables (e. g. , species richness or mean species abundance) (Appendix A). PREDICTS uses a hierarchical mixed-effects model to assess how a range of site-level biodiversity metrics respond to land use and related pressures, using a global database of 767 studies, including over 32 000 sites and 51 000 species from a wide range of taxonomic groups  (Hudson et al.
Both 1 • rasters and a table with values for each IPBES region and the globe were provided by each modeling team for each output metric. To measure the individual and synergistic impacts of landuse and climate change on biodiversity and ecosystem services, models accounting for both types of drivers were run three times: with land-use change only, with climate change only, and with both drivers combined. For instance, to measure the impact of land use alone, the projections into 2050 were obtained while retaining climate data constant from the present (2015) to the future (2050). Similarly, to measure the impact of climate change alone, the climate projections into 2050 (or 2070) were obtained while retaining the land-use data constant from the present (2015) to the future (2050). Finally, to measure the impact of land-use and climate change combined, models were run using projections of both land-use and climate change into 2050 (or 2070).
When models required continuous climate time-series data to hindcast to 1900, data from years in the time period 1951 to 1960 were randomly selected to fill the data missing for years 1901 to 1950 from the ISIMIP 2a IPSL dataset. Models that used multi-decadal climate averages from WorldClim (i. e. , InSiGHTS, BILBI) assumed no climate impacts for 1900. Geosci. Model  Dev. , 11, 4537-4562, 2018  www. geosci-model-dev. net/11/4537/2018/ certainties were explored by each model reporting the mean values of its metrics, and where possible the 25th, 50th, and 75th percentiles based on the parameterization set specific to each model, which can be found in each model's key manuscripts describing the modeling methods. When combining the data provided by the different models, the average and the standard deviations of the common metrics were calculated (e.
umd. edu/data. shtml (Hurtt et al. , 2017). The climate datasets used in BES-SIM can be downloaded from the respective websites (https://www. isimip. org/outputdata/ (Inter-sectoral Impact Model Intercomparison Project Output Data, 2017), http://worldclim. org/version1, Hijmans et al. , 2017). Distribution of suitable habitat (land) estimated from climate and land-use data using a statistical model on species presence and climate and land-use classifications, calibrated by historical data. Please see Table  S2   Proportional species richness of each species group is a power function of the sum of the areas of each habitat in a landscape, weighted by the affinity of each species group with each habitat type. Species richness is calculated by multiplying the proportional species richness by the number of species known to occur in the area.
Acknowledgements. HyeJin Kim ,  Inês Santos Martins ,  Florian Wolf ,  Carlos Guerra , and  Henrique M. Pereira  are supported by the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( FZT 118 ). Isabel Maria Duarte Rosa  acknowledges funding from the  European Union 's  Horizon 2020 research and innovation program  under  Marie Sklodowska-Curie  grant agreement no. 703862. Paul Leadley  is supported by the  LabEx BASC  supported by the  French "Investment d'Avenir" program  (grant  ANR-11-LABX-0034 ). George C. Hurtt  and  Louise Parsons Chini  gratefully acknowledge the support of the  DOE-SciDAC program  ( DE SC0012972 ). Almut Arneth ,  Andreas Krause ,  Benjamin Quesada , and  Peter Anthoni  acknowledge support from the  Helmholtz Association  and its  ATMO Programme , and  EU   FP7  project  LUC4C.
610028. David Leclère  and  Petr Havlík  are supported by project  SIGMA -Stimulating Innovation for Global Monitoring of Agriculture -and its Impact  on the Environment in support of GEOGLAM, funding from the  European Union 's  FP7 research and innovation program  under the environment area, grant agreement no. 603719. Tomoko Hasegawa ,  Haruka Ohashi ,  Akiko Hirata ,  Shinichiro Fujimori ,  Tetsuya Matsui , and  Kiyoshi Takahashi  are supported by the  Global Environmental Research  ( S-14 ) of the  Ministry of the Environment of Japan. Tomoko Hasegawa ,  Shinichiro Fujimori , and  Kiyoshi Takahashi  are supported by  Environment Research and Technology Development Fund   2-1702  of the  Environmental Restoration and Conservation Agency of Japan. Mike Harfoot  is supported by a  KR Rasmussen Foundation  grant "Modelling the  Biodiversity Planetary Boundary and Embedding Results into Policy"  ( FP-1503-01714 ).
Method: We searched the PsychInfo, Pubmed, and ScienceDirect databases using the key words "cognitive remediation," "schizophrenia," "cerebral activity," and "magnetic resonance imaging," in order to select studies investigating the effects of cognitive remediation on patients with schizophrenia. The studies selected had to present their approach in detail and measure its impact in terms of both cerebral activity and cognitive function, both before and after therapy. We divided the studies into two groups, those using the strategy method and those using the training method. Results: Eight studies were included in the review, four for the strategy method (88 patients, 44 of whom underwent remediation) and 4 for the training method (87 patients, 43 of whom underwent remediation). The analysis of the results of this study indicates that the training method is capable of activating more the targeted brain areas than the strategy method.
| METHOD We conducted a systematic search using the PRISMA criteria  (Moher, Liberati, Tetzlaff, & Altman, 2009)  in the PsychInfo, Pubmed, and ScienceDirect databases. The terms used for the search related to the type of remediation used "cognitive remediation," the "schizophrenia" population and the measurement of cerebral activity with the key words "cerebral activity" and "magnetic resonance imaging. " Our inclusion criteria were as follows: we selected randomized studies investigating the effects of cognitive remediation in adults with schizophrenia; the effects of the remediation on cerebral activity and cognition had to have been assessed before and after remediation for the purposes of comparison; the remediation program had to be described in detail and focus on cognitive function and not solely on social cognition.
Indeed, there are no simple training methods for impairment of social cognition, and it is therefore difficult to compare the two methods on this point. Over the course of the study, a table was completed showing the main data from each article (remediation technique, population, participant groups, the control therapy, the task used to measure cerebral activity, detailed information on the cerebral effects, the target cognitive impact, and the actual cognitive impact). In order to analyze and compare the results, we divided the studies into two categories according to the two methods investigated as follows: strategy or training. The studies were assigned to the two categories based on the training method or strategy method after careful examination of the programs used. For the studies assigned to the strategy category, the therapist was involved in the rehabilitation process, providing the participants with strategies to use themselves to improve performance, which they can reproduce in their daily lives.
| RESULTS The search criteria are set out in the PRISMA study selection flowchart (Figure  1 ). The search found 555 articles. Five hundred and forty-three studies were removed as they did not meet the eligibility criteria: use of imaging technique, use of a detailed cognitive remediation program, and patients with schizophrenia. Subsequently, any studies included twice over from the searches in different databases were removed, leaving a total of eight selected studies. The patients included in these studies were stable and receiving treatment. Three studies demonstrated effects on connectivity, and a number of studies found correlations between increases in certain types of cerebral activity and improvements in cognitive function including attention, working memory, verbal memory, and cognitive control.
Screening for eligibility Potentially eligible patients will be identified by medical and nursing staff in the ED and also by the Emergency Medicine Research Group of Edinburgh (EMERGE) researchers (part of the patient's direct care team) screening in accordance with EMERGE Research Governance: Data protection and Confidentiality SOP current version. Eligibility will be confirmed by the study researcher delegated to the study. Non-recruited but potentially eligible patients aged 16 years or over, triaged to high dependency (HD)/ immediate care (IC) areas will be identified by a search of all ED attendances at the end of the study to estimate the proportion of ED attendances who have participated in the study.
Treatment allocation Participants will firstly be allocated a participant study number. Basic demographic data and the participant's baseline observations will then be collected from each participant at enrolment and recorded in the case report form (CRF). The study researchers will then allocate the participant a SNAP40 device. Each device has a unique 'identifier' that will be recorded on the CRF and also on a separate recruitment log. This will allow the clinical vital sign and clinical alert data from each participant to be collected and recorded on the CRF along with the alerts from the SNAP40 device for the same participant. If a new device (e. g. because of a depleted battery) replaces a SNAP40 device already worn by a participant, the unique ID of the new device along with the reason for replacement and time it is assigned will be recorded on the CRF.