dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Selection The search results will be combined in EndNote reference management software and duplicates will be removed  [67]. The deduplicated records will be imported into Covidence (Veritas Health Innovation Ltd), a tool for managing and documenting studies during the review process  [68]. Screening will occur in 2 phases: level 1 for title and abstract screening and level 2 for full-text screening. To ensure consistency, a pilot test will be conducted on 10 titles and abstracts by all members of the research team to evaluate agreement among reviewers. Any discrepancies and ambiguity regarding the inclusion and exclusion criteria will be resolved through discussion, and another pilot test will be conducted until a consensus is reached. Following the pilot testing, the titles and abstracts will be screened by 2 reviewers against the predetermined inclusion criteria.
The authors will use Covidence to vote yes, no, and maybe. Abstracts with uncertainty will undergo full-text review. Potentially relevant sources will be assessed at the full-text stage by the same 2 independent reviewers. Any disagreements in voting between reviewers will be resolved through discussion with the senior responsible investigator at both levels of screening. The reviewers will meet weekly with the research team to discuss the screening process. The following inclusion criteria will be used to screen the literature: (1) be published in the English language in a peer-reviewed journal and include primary data on wearables and voice-activated virtual assistants as the primary smart technology interventions; (2) examine the interaction between smart technologies and older adults over a period of 6 months or more; (3) involve study samples of participants only aged 55 years or older.
Study Extraction The research team will create a data extraction form to extract data related to the study aims and questions. Thus, the data extraction process will cover various categories, such as study characteristics (eg, the author's name, year of publication, country of origin, study purpose or aim, study design, data collection methods, sample size, and study setting), participant characteristics (ie, sex, gender, and other identity constructs), underlying models, theories and frameworks used to inform the research, findings, implications for the coadaptation of technology process, and any quality or limitations identified in the study. We will look for instances of shifts in user behavior patterns over time, reflect on adjustments in interactions with smart technologies, and identify instances where both users and technologies engage in reciprocal feedback loops, thereby leading to iterative adaptations.
Three reviewers (KK, AG, and JM) will first pilot the extraction tool on a subset of the articles. The data extraction tool will be modified and revised as needed during the extraction process for each study included. Any disagreements between the reviewers will be resolved through discussion or by involving additional reviewers if necessary. Next, 1 author (KK) will extract data from the included studies. All relevant data from the studies included will be extracted using free text. By extracting data using free text, the reviewers can capture a wide range of details and nuances, providing a detailed overview of the studies' content and enabling a thorough analysis of the research findings. The senior responsible investigator will then review all extracted data for accuracy.
Data Analysis and Presentation The scoping review will use 3 reporting and presentation strategies to provide an overview of the literature. First, a basic numerical account will be provided, presenting information on the number of included records, the year of publication, the types of study designs included details of included participants, and the outcome measures, among other relevant factors. This numerical summary will give a quantitative overview of the body of literature. Next, a directed content analysis  [69]  will be conducted on the included literature, focusing on the purpose of coadaptation techniques used over time, study findings related to outcomes and experience, and implications for technology design and future research. The content analysis will involve inductive and deductive categories.
The reactions were performed for 2 min at 50 • C and 10 min at 95 • C, followed by 40 cycles of two-step amplifications of 15 s at 95 • C and, finally, 10 min at 60 • C. Data were acquired on the BioMark real-time PCR system and analyzed using the Fluidigm real-time PCR Analysis software to obtain cycle threshold values, which for positive samples ranged between 16. 3 and 26. 5. False positive or inconclusive (unreliable) results were declared at Ct > 30. Primers and probes were evaluated before use for their specificity against cDNA reference samples (see  [26]  for details). One negative water control was included per chip. To determine if factors present in the sample could inhibit the PCR, Escherichia coli strain EDL933 DNA was added to each sample as an internal inhibition control, using primers and probes specific for the E.
Sequencing and Phylogenetic Analysis of WNV Amplification of partial NS5 gene by conventional pan-flavivirus PCR was performed on positive mosquito pool homogenates and virus isolates, as described by Weissenböck et al. [30]. Amplification was achieved under the following thermal cycling conditions: 30 min at 50 • C, 15 min at 95 • C, 45 cycles at 94 • C for 30 s, 60 • C for 30 s, 72 • C for 1 min and 10 min at 72 • C. Resulting amplicons were visualized by electrophoresis (30 min at 90V) on a 1% agarose gel in the presence of a 1kb ladder (Hyperladder, Bioline, London, UK). Sanger sequencing was performed in both directions and the resulting nucleotide sequence was identified by basic alignment search tool (BLAST) in the Genbank database (https://blast. ncbi. nlm. nih. gov/, accessed on 30 November 2022).
We therefore imputed these missing data prior to any further analysis. We superimposed separately any concerned mandibles with its reflected counterpart or any concerned skull with its mirror image, supplied the missing landmarks from the counterpart and transformed the coordinates back. This was not possible for landmarks on the skull midplane or those missing on both sides and such skulls or mandibles were excluded from the study. Landmark digitization was performed Stratovan Checkpoint software (Stratovan Corporation, https:// www. strat ovan. com/ produ cts/ check point). GPA of skulls and mandibles was performed in packages Morpho  (Schlager, 2017)  and geomorph  (Adams et al. , 2020)  for R (R Core Team, 2021). Missing data were imputed, and the results visualized using R package rgl  (Adler & Murdoch, 2020)  and custom R functions available at https:// github.
0) relative to high-elevation ones (coded by 0. 00). We fitted PLS by NIPALS algorithm  (Wold, 1975) , an iterative procedure returning each time a pair of components that co-vary as much as possible. Every next pair is fitted to variation, which is left after projecting out variation accounted for by the preceding pairs. We used "PLS2" variant of the algorithm, which distinguishes predictor and response variables  (Rosipal & Krämer, 2005). The algorithm was implemented in R function 'nipals' also available at https:// github. com/ onmik ula/ gmtoo ls. PLS was conducted separately in each genus on the species mean shapes, which makes the analysis robust to unbalanced sample size and possible differences in intraspecific variation patterns. The individual shapes were then projected on the resulting PLS components (like in between-group PCA;  Cardini et al.
As PLS comes with no test of significance, we also used the analysis of variance using distance matrices as implemented in adonis2 function of R package vegan  (Oksanen et al. , 2022). The fitted linear model was fully factorial with genus, elevation zone and their interaction as predictors and Euclidean distance matrix between PC1-20 scores of species mean shapes as a response. The significance was tested with a permutation test (99,999 iterations). The elevation zones were encoded as in the first column of the matrix A and hence the proportionality of shape change was assumed. In addition, we also compared skull and mandible size among elevation zones in each genus. The elevation zones were treated as levels of an ordered factor and significance of the difference between them was tested by a linear mixed effects model with elevation zone as the fixed effect and species as a random effect.
The models were fitted using the maximum likelihood criterion in R package nlme  (Pinheiro et al. , 2020). The model including the elevation zone was compared to the null (intercept-only) model by Akaike Information Criterion (AIC;  Akaike, 1974). In models with ΔAIC > 2 we performed Tukey's honest significance post hoc tests  (Tukey, 1949) , as implemented in R package multcomp  (Hothorn et al. , 2008). Finally, we fitted a model with the fixed effects of elevation and genus and tested for significance of their interaction to detect possibly discordant trends in size. Size variation was visualized in R using packages lattice  (Sarkar, 2008)  and latticeExtra  (Sarkar & Andrews, 2019).
Results The three genera are clearly distinct in their skull and mandible shape (Fig. 3 ) on the first principal plane defining the shape space. In the skull, the first principal component (PC1) separates Arvicanthis and Stenocephalemys (Murinae) from Lophuromys (Deomyinae), whereas PC2 singles out Arvicanthis. In the mandible, all the genera are separated along PC1 and Stenocephalemys stands apart along PC2. The figure also suggests elevation gradation to account for a part of shape variation, because in both datasets the ordination of species in at least one genus clearly follows the zonation pattern. For clarity, Fig. 3  shows species means, although PCA was conducted on individual shapes. The analysis of variance showed both skull and mandible shape being significantly different between genera (P < 0.
Acknowledgements For help in the field and with logistics, we acknowledge  Y. Meheretu ,  R. Šumbera ,  V. Mazoch ,  T. Aghová ,  A. Ribas ,  J. Šklíba ,  M. Lövy ,  D. S. Kostin ,  A. A. Martynov ,  A. R. Gromov ,  D. Yu. Alexandrov ,  M. Kasso , and all local collaborators.
Subsequently, they were once again asked to estimate their confidence level in their decision using the same methodology as before (i. e. "confidence 2"). The experiment concluded with the collection of demographic data such as age, gender which were used as covariates in the analyses to enable better generalization of the results. On the final page, a message was displayed presenting the study's objectives and stating that although the information provided for decision-making was accurate, it was partial and limited, and did not allow for a holistic approach to the problem at hand.
Positive (genomic DNA from a pure culture of C. parvum IOWA oocysts) and negative (using sterile water as a template) controls were included in both reactions. Following separation and excision from a 2% agarose gel, DNA was extracted using GeneJET Gel Extraction Kit (ThermoFisher Scientific). All positive samples were then bidirectionally sequenced (Eurofins Genomics) and chromatograms were manually assessed for quality, with ambiguous bases trimmed. For species level identification, BLAST searches were performed using obtained sequences as queries against the nucleotide database in GenBank. Following alignment with reference sequences, polymorphisms were identified.
Positive and negative controls were included in both reactions. Following separation and excision from a 2% agarose gel, DNA was extracted using GeneJET Gel Extraction Kit (ThermoFisher Scientific). All positive samples were then bidirectionally sequenced (Eurofins Genomics), and chromatograms were manually assessed for quality, with ambiguous bases trimmed. Subtypes were determined using established standard nomenclature  (Sulaiman et al. , 2005). BLAST searches were performed using newly generated sequences as queries against the nucleotide database in GenBank (http://blast. ncbi. nlm. nih. gov/Blast. cgi). Following alignment with reference sequences, polymorphisms were identified.
Phylogenetic analysis The obtained sequences were aligned with each other as well as reference sequences from GenBank by MAFFT v. 7 (https://mafft. cbrc. jp/alignment/server). Sequence alignment was manually inspected using BioEdit v7. 0. 5. 3 (https://bioedit. software. informer. com). Best DNA/Protein phylogeny models were selected using the MEGA11 software  (Tamura et al. , 2021). The Tamura 3-parameter model  (Tamura, 1992)  was selected. Phylogenetic trees were inferred using maximum likelihood (ML), with the substitution model that best fit the alignment selected using the Bayesian information criterion. Bootstrap support for branching was based on 1000 replications. An SSU sequence from Monocystis agilis (Accession number: AF457127) was used to artificially root ML tree.
Data analyses All the data visualization and statistical analysis were done with R Version 4. 1. 2 (R Core Team, 2021) and RStudio  (RStudio Team, 2021)  including the R packages "lme4"  (Bates et al. , 2015) , "MuMIn"  (Bartoń, 2020) , "car"  (Fox and Weisberg, 2019) , "dplyr"  (Wickham et al. , 2020) , "AICcmodavg"  (Mazerolle, 2020) , "effects"  (Fox, 2003; Fox and Weisberg, 2019) , "lattice"  (Sarkar, 2008) , "corrplot"  (Wei and Simko, 2017)  and "ggplot2"  (Wickham, 2016). The landscape analysis was done with the package "landscapemetrics"  (Hesselbarth et al. , 2019)  and the Shannon's diversity index of phytoseiid mite communities and Pielou's evenness index were calculated with the package "vegan"  (Oksanen et al. , 2019). Data exploration (e. g. outliers, distribution of the response variable, variance homogeneity) was done following  Zuur et al.
(2010). Statistical modelling was performed for the phytoseiid mite datasets from Bordeaux, Palatinate, Leithaberg and Târnave (corresponding dataset from 2020, Târnave 2020 from here on). The phytoseiid dataset from Târnave (corresponding dataset from 2019, Târnave 2019 from here on) contained too many missing values and the phytoseiid dataset PDO Montilla-Moriles from 2021 too many zero counts with a very low population density overall, and therefore these datasets were not modelled, to avoid possible misinterpretations  (Zuur et al. , 2010). The response variable, phytoseiid mite densities per 100 cm 2 vine leaf area, was transformed with log 10 (log 10 (y + 1)) (Curran-Everett, 2018; O'Hara and Kotze, 2010) for fitting linear mixed models (LMM) and linear models (LM), to use Gaussian distribution and not violate the model assumption of normal distributed residuals  (Zuur et al.
, 2009). LMM were used for the phytoseiid data from Bordeaux with the paired vineyards nested within the landscape as random effect. LM were used for the other phytoseiid datasets from Leithaberg, Palatinate, Târnave 2020 due to a very low variance of the random effect estimated at zero, leading to boundary issues  (Bolker et al. , 2013). We analysed the effect on the response variable through the following explanatory variables: farming system, vegetation type (with the exception of Bordeaux, see Table  1 ), date (i. e. , sampling date), aAPTLc, proportion of semi-natural habitats cover and vineyard cover. For Palatinate, the additional landscape variable with or without hedge was added. The vine leaf hairiness index was added as explanatory variable for Leithaberg and Târnave 2020. This variable could not be used for modelling the data from Palatinate and Bordeaux because in each region only one vineyard had a different leaf hairiness index (Table  S4 ).
Furthermore, we also added an interaction between farming system and date, in accordance with the wine-growing regions. Collinear explanatory variables with cor ≥ ± 0. 5 were not included in the same models. This was only the case for the proportion of semi-natural habitats cover versus vineyard cover in Bordeaux (cor = -0. 81) and Palatinate (cor = -0. 68) and farming system versus aAPTLc in Târnave 2020 (cor = -0. 7). The variance inflation factor (VIF) of 2 was additionally used as criterion for exclusion of explanatory variables in the models  (Zuur et al. , 2010). Accordingly, global models were computed  (Bartoń, 2020; Burnham and Anderson, 2002)  for the phytoseiid datasets from Bordeaux, Palatinate, Leithaberg and Târnave 2020 with all possible combinations of the explanatory variables that passed the above mentioned criteria.
Acknowledgment We would like to thank all winegrowers who provided their vineyards as sampling sites. Furthermore, we are grateful to  Lina Weissengruber ,  Katharina Neubacher ,  Božana Petrovic ,  Ahmad Aldelemi ,  Argyroula Tsormpatzidou ,  Beatriz Sánchez Romera  and  María Luisa Fernández-Sierra  for support during field and laboratory work. We are thankful for  Yang Chen  who illustrated the wine-growing region map and for the support of the statistical analysis by  Friedrich Leisch.
We analyze the concentration-driven outputs of temperature and land and ocean carbon cycle fluxes simulated by RCMIP SCMs  (Table 1)  and CMIP6 ESMs (Table 2 ) under the eight available SSPs. The SSPs are developed by IAMs that describe the social and economic components that can determine future climate change  (Ackerman et al. 2009; van Vuuren et al. 2017 ) (Table  3 , Fig. 1 ). In CMIP6, the emissions of different greenhouse gases (GHGs) from IAMs are then harmonized for the base year to their respective values in the historical inventories using the Aneris software  (Gidden et al. 2019 ) and converted to concentrations by the SCM MAGICC version 7 (Fig. 1c ). The land-use and land cover data from IAMs are converted to a readable gridded format suitable for ESMs within the harmonization of global land-use change and management version 2 (LUH2) project  (Hurtt et al.
For ESMs, we used anomalies relative to the long-term mean piControl values, removing any residual trend in the piControl experiment. For SCMs, we estimated the NBP as the difference between their net land-to-atmosphere flux CO 2 and LUC emissions from IAMs provided in the SSP database (except for OSCAR, which provided both land carbon sink and LUC emission outputs). When ESMs provided NBP and fLuc estimates, we calculated the "natural land carbon sink" (excluding LUC emissions) by adding fLuc (positive to atmosphere) to NBP. We used the CO 2 concentration by  Meinshausen et al. (2020)  to estimate G Atm for Eq. 1. To evaluate GSAT estimates of the models, we used the mean of the following observationally based datasets: Cowtan and Way v2 (Cowtan and Way 2014), GISTEMP v4 (NASA Goddard and Institute for Space Studies 2020), CRU TS 4.
00  (Harris et al. 2014) , University of Delaware 4. 01  (Lawrimore et al. 2011) , and reanalyses-observation hybrids -CRU-NCEP and Princeton University  (Sheffield et al. 2006 ). To evaluate historical carbon fluxes, LUC, and FF emissions with associated uncertainties, we used the Global Carbon Budget 2021 (GCB2021) dataset  (Friedlingstein et al. 2021) , historical data from  Gruber et al. (2019) ,  Khatiwala et al. (2009), and Li et al. (2016). GCB2021 is a synthesized dataset of major global carbon budget components, including FF emissions based on energy statistics and cement production data, LUC emissions based on land use and land-use change data and bookkeeping models, ocean carbon uptake based on estimates of global ocean models and observationally-based data products, and land carbon sink based on dynamic global vegetation models  (Friedlingstein et al.
2021). Historical data from  Gruber et al. (2019)  include ocean carbon flux based on observations from US Global Ocean Carbon and Repeat Hydrography Program. Historical data from  Khatiwala et al. (2009)  include reconstruction of anthropogenic ocean carbon flux using tracer observations. Flux estimates of  Gruber et al. (2019)  and  Khatiwala et al. (2009)  include FF emissions from  Boden et al. (2009)  and LUC emissions based on bookkeeping models. For the two datasets,  Khatiwala et al. (2009)  and  Boden et al. (2009) , we calculate the land carbon flux using Equation  1. Finally,  Li et al. (2016)  provide estimates of land and ocean carbon uptakes, as well as LUC and FF emissions. These estimates are constrained with the Bayesian fusion approach and averaged over 5-year periods between 1980 and 2014.
LUC emission data are largely inconsistent between ESMs and IAMs (used in SCMs) partly because of the discrepancies that emerge during the translation of the data from IAMs to ESMs  (Melnikova et al. 2022). ESMs estimate lower positive LUC emissions (net source to the atmosphere), possibly because they do not include forestry and managed land practice. They also estimate smaller negative LUC emissions (net sink to the land) relative to emissions created by IAMs. The discrepancies may arise because LUC emissions reported by ESMs include deforestation (biomass loss during deforestation), wood harvest, and the carbon release by harvested wood products, but do not account for forest regrowth and legacy soil carbon decay or gains  (Melnikova et al. 2022 ). Among SCMs, only one model, OSCAR, estimates LUC emissions via a bookkeeping approach.
Both ESMs and SCMs estimate higher future compatible FF emissions compared to those simulated by IAMs (Fig. 3c ). This issue has been previously discussed by  Liddicoat et al. (2021)  and may be related to lower estimates of land and ocean carbon uptakes by MAGICC7. 0, which was used with IAMs to generate the CMIP6 ScenarioMIP input CO 2 concentrations. MAGICC7. 0 (slightly different version from MAGICCv7. 5. 1 of RCMIP phase 2) was calibrated to CMIP5 ESM carbon cycle to include permafrost CO 2 and methane feedbacks  (Meinshausen et al. 2020; Nicholls et al. 2021 ). MAGICCv7. 5. 1 estimates a lower land carbon uptake than observationally based datasets during the historical period and than the model ensemble means in future scenarios (Figs. 1, S15-16). However, the lower land carbon uptake estimates by MAGICCv7.
Model outliers We defined outlier models simply as the ESMs/SCMs that estimate maximum/minimum global land and ocean carbon uptake (maximum/minimum, or upper/lower ends) over 1850-2014 historical and 2015-2100 future SSP scenarios (Tables  S1  and S2 ). The model outliers vary depending on the target carbon flux and SSP scenario, e. g. , low vs. high FF or LUC emission pathways, increasing CO 2 concentration, and temperature vs. mitigation pathways. Besides, the models that provide data vary with the target scenario. In the case of land carbon flux, CanESM5 and CNRM-ESM2-1 estimate the highest NBP during historical and future periods among ESMs. These two ESMs do not include a nitrogen cycle explicitly, a process that is shown to limit the land carbon uptake through nitrogen limitations of plant growth  (Arora et al.
Acknowledgements We thank  Pierre Friedlingstein  and  Mike O'Sullivan  from the  University of Exeter  for answering our questions on GCB2021,  Thomas Gasser  from  IIASA  and  Yann Quilcaille  from  ETH Zürich  for answering our questions on OSCARv3. 1 outputs,  Zebedee Nicholls  from  Australian-German Climate & Energy College, University of Melbourne , for answering our questions on MAGICC7 outputs. We are grateful to  Samar Khatiwala  from  University of Oxford  for providing carbon fluxes data. The IPSL-CM6 experiments were performed using the HPC resources of TGCC under the allocations 2019- A0060107732 ,  2020-A0080107732 ,  2021-A0100107732 , and  2022-A0120107732  (project gencmip6) provided by  GENCI  ( Grand Equipement National de Calcul Intensif).