dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: We thank  Stéphane Morisset  ( Stéphane Morisset Etudes and Consulting, Pérouges, France ) for the statistical analysis,  A. Wolfe  ( Chaumont, France ) for revising the draft and  Olaf Mercier  ( Hôpital Marie Lannelongue, Le Plessis-Robinson, France ) for supporting this project. Data sharing: Anonymised participant-level data, the study protocol and informed consent form, and the statistical analysis plan will be available from the corresponding author upon reasonable request, after study publication and after signature of a data-sharing agreement.
| Sample/participants The sample was comprised of adolescents attending 15 public senior high schools (general and vocational) in three regions of France, with most recruited in Paris Region. All schools were located in dense to semi-dense urban areas. Participants were included if they were between the grades of 10 and 12, gave oral consent to participate in the study, and had the written consent of a legal guardian. A total of 4058 adolescents took part in the study (participation rate: 65%); however, 21 questionnaires had inconsistent or missing responses and were excluded. Thus, the total sample included in this study was 4037 adolescents. Participants' mean age was 16. 1 years (SD = 1. 1; 3. 3% were 13-14, 87. 1% were 15-17 and 9. 1% were 18-21, with missing data for 0. 3%), and 60. 2% were female.
| Data collection Data were collected from May 2018 to February 2021. After the agreement of the headmaster was obtained, the school's professionals were informed of the study. The study was presented to adolescents during class and the following week consent to participate was collected from the adolescents and their parents or legal guardians. The participants then completed a self-reported and anonymous questionnaire during class under the supervision of a researcher and a teacher. The questionnaire was available in paper-and computerbased versions. It was composed of eight scales, part of which will be presented in this article. The questionnaire took approximately 30 min to complete.
Moreover, the prevalence rates in Australia and New Zealand were obtained through census data and by surveying households. Studies identifying YC/AYC by data collected directly among youth in schools systematically report higher rates. Nevertheless, our prevalence is Abbreviations: M, mean; SD, standard deviation; χ 2 should be taken with caution as theoretical frequency is less than 5. higher than in most studies and we can wonder if French adolescents overestimate or more easily report the support they provide compared with adolescents in other countries. This should be investigated in future studies with international comparisons to better understand this result. The high prevalence of AYC in this study may also be related to the identification criteria used. Indeed, we chose to include AYC whether they were living with their ill/disabled relative or not.
Strengths and Limitations This systematic literature review allows the social and emotional aspects of the VH determinants to be revealed. VH is a complex phenomenon, and discussion, by comparing these results with the literature, showed interaction between the different determinants and their transferability to other populations. Our search strategy may have led to the omission of studies. Although the search equation was developed with the help of a librarian, it may have been too stringent. In addition, other search databases could be included such as "Embase" or "PsychINFO". The quality of selected articles (18/32 items on average) requires a cautious interpretation of the results, even if they are comparable to the literature on the subject.
Nor did it permit direct comparisons to be made between men and women within couples. The authors therefore used a panel of respondents who were being followed prior to, and during, the pandemic to complement the quantitative data from the national survey. An obvious way for researchers to access large-scale background data relating to families' experiences of the pandemic was to draw on existing national datasets. Whether the analysis of findings from large-scale surveys constitutes "dataset re-use" or "secondary analysis"  (Brannen et al. , 2021 ) is a moot point given that some of these datasets were ongoing national panel studies that were adapted to include questions relating to COVID-19. The advantage of secondary analysis is that it involves bringing new research questions to existing data  (Heaton, 2004).
In the case of coastal protection, the effect size was calculated as the Euclidian difference between MPAs and controls, hence the separate scale provided. Values are presented as mean values ± 95% confidence interval. Black dots indicate effect sizes that do not overlap zero and white dots those that overlap zero. Sample sizes (i. e. , number of studies) of vote counting and meta-analysis results are indicated by n values. *In the case of C sequestration from fish biomass, the effect size was calculated from three previous meta-analyses, representing data from many more individual studies. (D-F) Effect of habitat type (D), protection level (E), and presence of a fully protected area (F) on the capacity of MPAs to provide climate benefits. Colors, sample size, and x axis have the same meaning as in (C).
CONDITIONS UNDER WHICH MPAs DELIVER CLIMATE BENEFITS We found sufficient data to investigate the effect of the level of protection, age, and size of MPAs on four climate pathways (C sequestration, biodiversity, CPUE, and income). Across all four pathways, only full and high levels of protection resulted in mitigation or adaptation benefits (Figure  2E ). In contrast, lower levels of protection generated no benefits. Increases in species richness and in fishers' income only occurred for fully protected areas, where no fishing is allowed. Increases in CPUE and C sequestration were also achieved by highly protected areas, where low-impact fishing is allowed. However, this was only the case when a fully protected area was also present in the MPA considered (i. e. , a multi-zone MPA with one zone being a fully protected area).
Review To make sure no important article was missed, the database obtained through our systematic literature searches was complemented with studies included in previous systematic reviews relevant to one of our pathways. The following systematic reviews were used to complete our database: d For social pathways  25  d For C sequestration  13,84,85 d  For resistance and recovery  23  Additional articles identified from expert knowledge or from citations in reviewed publications were also added to our database. All systematic reviews were performed using Web of Science with no time limitation and using the ''topic'' (titles, abstracts, and keywords) search section. The dates when the searches were performed are detailed in Data S1 (accessible through the Zenodo link provided in the ''data and code availability'' section).
For mitigation pathways and coastal protection, the system of interest was enlarged to preserved habitats because mangroves and marshes benefit from a variety of conservation measures that act similarly as MPAs by regulating natural resource extraction. To allow a maximum of results to be found, the search string broadly included the names of the habitats of interest (Data S1; see ''data and code availability'' section), and the articles were selected when they compared preserved habitats with degraded controls (see ''screening criteria'' section). Screening criteria for the vote-counting approach Screening of articles was performed using a three-stage process: screening of titles, screening of abstracts, and screening of full texts. Some criteria were common to all systematic reviews, and others were specific to pathways.
Common screening criteria were as follows: d The article provided primary data collected on the field or in a lab (models, reviews, and syntheses were excluded, as well as studies using datasets already presented in previous studies). d The system studied was a marine or coastal habitat (freshwater systems were excluded). d The study compared an MPA site with a non-MPA site (or a preserved/ restored habitat with a degraded habitat in the case of mitigation pathways and coastal protection), using a before-after or control-impact design (before-after-control-impact designs were extracted when available). d Studies with pseudo-replication were excluded. In the case of studies comparing MPAs with control sites (all studies documenting adaptation pathways except coastal protection), screening criteria were as follows: d The control site must be outside of the MPA (comparisons between notake zones and buffer zones or partially protected areas were excluded).
d The MPA must be a permanent closure (seasonal fishery closures were excluded). Screening criteria specific to each pathway are detailed in Table  S2. Title and abstract screening were conservative to avoid excluding any relevant studies and were used to phase out studies that were clearly irrelevant to our search. Studies that passed our title screening phase were exported from Web of Sceince in an Excel file with all their relative information (including abstracts). Abstracts of the selected articles were then screened using the abstract screener interface provided by the {metagear} package in R Core Team (2021). 86 This package allows us to screen abstracts without showing authors or the journal's title, limiting sources of bias in the selection process. Articles selected after abstract screening were downloaded, and their text was fully read to assess whether they were relevant to our search criteria.
Number of values extracted per study In the following cases, several values were extracted from a same article: d When different MPAs were studied. d When different ecological groups (fish, fished invertebrates, corals, algae, seagrass. ) were studied. d When different stakeholder groups (fishery, tourism, recreational, and coastal community) were studied. d When MPA zones with different levels of protection were studied. In all other cases, values were averaged between categories (e. g. , different species of the same taxon, different religions within a community. ) to avoid over-representing studies. When time series of values were presented for a given MPA, only the most recent data point was kept as the MPA ''after'' value, and the data point closest to before MPA establishment was kept as the MPA ''before'' value.
Qualitative data extracted from screened articles For each value extracted, the following information was also extracted: d Design of the study: before-after (BA), control-impact (CI) or BACI. For social studies, the category ''perception'' when MPA outcomes were based on interviews and stakeholders' perceptions. d MPA name, MPA size (km 2 ), and MPA age (year) at the time of data collection. MPA age was counted from the moment when the MPA was enforced rather than announced to account for years during which marine ecosystems were effectively protected. When MPAs underwent several stages of extension, we counted MPA age from when the latest extension was carried out. When MPA features were not reported in the publication, we used information from the MPA atlas (https://mpatlas. org/). d The level of protection of the zone studied by the MPA and, when present, the size of the fully protected area in the MPA studied (km 2 ).
Levels of protection were determined according to the regulation-based classification system for MPAs. 87 In addition to information provided in the study, complementary searches were performed using the MPA atlas and the official management declarations of MPAs to determine the level of protection. d Continent, oceanic region, climate of the study location. d Ecosystem, taxon, and species studied. d Type of governance and of stakeholder studied for social pathways. d Pathway, indicator, unit, and type of error provided in the study. One spatial point was mapped for each data point used in vote counting and meta-analysis. Spatial points were located at the centroid of the protected area, as found in the MPA atlas (https://mpatlas. org/). Mapping was carried out using the {sf} package to manipulate vectors, {maps} to create the background layer, and {tidyr} to manipulate datasets.
Quantitative data extraction and preparation Quantitative results were extracted from the main text, tables, and figures of articles and from supplemental material. Mean values and errors were extracted from figures using the online application WebPlotDigitizer v. 4. 0 (Rohatgi 2018). Data extraction of the 16 pathways was carried out by three of the co-authors of this study. Data extraction of five studies was first carried out by all co-authors to make sure that the methodology was acquired by all. All data extracted by a co-author were double-checked by another coauthor to reduce extraction or calculation mistakes. All types of errors (standard errors, variance, confidence intervals) were converted into standard deviation (SD). Mean values and variance were calculated when results were presented across categories irrelevant to our study (see ''number of values extracted per study'' section), and sample size was added.
C sequestration from fish biomass The C sequestration pathway was investigated using keywords related to ''carbon storage'' or ''carbon sequestration,'' which returned very few results for fish. However, C storage in living fish biomass is directly proportional to fish biomass, which has been extensively measured in MPA research. Because those studies were not found through our dedicated literature search for C sequestration and because many synthetic studies and meta-analyses have already quantified the effect of MPAs on fish biomass, we based the value of this effect size on previous meta-analyses. The effect of MPAs on fish biomass was calculated from three previous meta-analyses. 15, 16, 61 Those three meta-analyses were chosen because they used completely distinct datasets, were among the most cited publications for effects of MPAs on fish biomass, and, together, encompassed all types of protection levels and all oceanic regions of the world.
For the effect of level of protection on fish biomass, we used values given in Lester et al. 16  for the effect of full protection, and values given in Zupan et al. 15  for the effects of low and high protection. Capacity of habitats to provide climate benefits Baseline capacity of habitats to provide C sequestration and coastal protection services, as well as global habitat extent, was based on values from the most recent and global available published reviews. Values and the reference of the study from which the values were extracted are detailed in Table  S4. We also performed a systematic search and meta-analysis following the methodology presented in the ''qualitative data extraction'' and ''quantitative data extraction'' sections to estimate the amount of C stored in macroalgae and the capacity of mangroves, seagrass, and macroalgae to provide acidity buffering.
This was done because we found no reviews that provided these values. The search string and date of the systematic search as well as the indicators selected were the same as those specified for the C sequestration and acidity buffering pathway (Data S1; see ''data and code availability'' section). Studies included in the meta-analysis on pH buffering capacity of marine habitats and on C storage capacity of macroalgae can be found in Data S3 and Data S4, respectively (see ''data and code availability'' section).
Resistance, recovery, and adaptation potential values Observed resistance and recovery were calculated through meta-analysis according to the methodology presented in the ''qualitative data extraction'' and ''quantitative data extraction'' sections. The search string and date of the systematic search as well as the indicators selected are detailed in Data S1 (see ''data and code availability'' section). Expected ecological adaptation was calculated as the mean value of all effect sizes contributing to ecological adaptation, i. e. , allelic richness (genetic diversity), species richness and Shannon index (biodiversity), temporal stability (stability), and reproductive output and recruitment (reproduction). The confidence interval was calculated by bootstrapping, using n = 1,000 iterations.
The resulting identification keys are provided in the appendix of this article (Figures  S1  and S2 ). Due to the fragmented state of the cranial bones, it was however often impossible to observe, on a given bone, every criterion required for identification. A probabilistic approach was therefore implemented: remains not identifiable to species were classified by degree of morphological proximity to different groups of species, according to a resemblance gradient. Data were then summarised by skeletal part, and the three lists of species obtained were compared with each other, in order to obtain a final list of the cyprinid species present, probably present, and possibly present at Ranchot.
e. 552 remains, and the metric was carried out on a random sample of 500 vertebrae. The surface conditions of the latter were also examined in keeping with the work of  Butler & Schroeder, 1998; Nicholson, 2000. A summary of the obtained results is presented later in this article. The detailed results of this work will be the subject of a specialist publication, in preparation. On the other hand, no bone from dippers' pellets could be obtained. In view of the very similar biological and ethological characteristics of the dipper to those of the kingfisher (size, feeding and dumping habits, nesting sites, etc. ), we considered that the data from the latter species could constitute an acceptable proxy. In order to undertake a comparison with the data of the accumulating agents, and to gain a better understanding of the taphonomic history of the collection, a survey of the surface conditions of all 9,328 remains of the Ranchot collection was carried out, noting for each the possible presence of pitting, rounding, deformation, and staining, as well as the presence of traces of fire marks and cutting marks, following the descriptions of  Asmussen (2009).
Acknowledgments: We wish to thank the professionals and individuals who provided us with valuable data and fish specimens, in particular to the staff of the SD39 of ONEMA ( G. Durand ,  P. Gindre ,  P. Chanteloube , and  S. Forgeois   DR2   ONEMA Bourg-Achard ). Thanks also to  M. El Bettah , in charge of the  Jura Federation for Fishing and the Protection of the Aquatic Environment , to  P. Falcy  of the  Joux Valley Sports Fishermen's Association  and to  O. Sommen , a fish farmer and member of the Salmon 2000 programme in the  Petite Camargue Alsace Nature Reserve. We also thank  L. Byrne  for the translation. Finally, the manuscript benefited from the comments of two anonymous reviewers, who are thanked. Funding information: This research was supported by regional funding through the project  Des derniers Néandertaliens aux premiers agriculteurs dans le massif du Jura et ses marges.
This process can be done using machine learning (ML) techniques which can effectively classify ADLs performed by people. Existing activity classification methods employ a variety of ML techniques to recognise activities, including the Hidden Markov Model (HMM)  [8] , Conditional Random Fields (CRF)  [9] , Random Forest (RF)  [10] , Support Vector Machine (SVM)  [11] , Naive Bayes (NB)  [12] , Decision Tree (DT)  [10]  and K-nearest neighbour (KNN)  [13]. Recently, increased attention has been given to deep learning (DL) techniques such as deep neural network (DNN), convolutional neural network (CNN), autoencoders (AE) and recurrent neural networks (RNN) in several fields because they have great impacts in terms of flexibility and performance. The accuracy of the ML and DL techniques to classify activities is an important measure that depends on the classifier parameters and on extracted features from a pre-segmented dataset.
After activity classification and anomaly extraction, a clean sequence of consecutive activities is formed. Finally, the sequence is trained using long short-term memory (LSTM) algorithm and its model is used to predict the next activity. A unified monitoring system with such features can provide useful information for elderly people who suffer from dementia, and for medical team to analysis elderly health and take preventive action once needed. A dataset from CASAS project called Aruba  [22]  and Cairo are selected for this study since its labelled activities are good example to represent the daily life pattern of elderly residents in smart homes. The usage of the proposed unified model can be extended and implemented for different smart home applications such as energy-saving or security monitoring and threat detection  [23].
II. RELATED WORKS A. ACTIVITY RECOGNITION Several datasets such as CASAS  [22] ,  [24]  and Kasteren  [25]  are provided for the public to test the ability of ML and DL classifiers to recognise human activities in smart homes. The Aruba dataset  [22]  that was collected as a part of the CASAS project suffers from class imbalance problem which reduces the performance of the classifiers to recognise some of the activities (i. e. , ''Resperate'' activity). Features are important factors that play significant roles in the performance of activity recognition techniques. Several studies have discussed feature extraction from different datasets. In  [10] , many features are extracted such as start and end time of activity, duration of activity, sensors states and location of activities. Then different ML classifiers are used such as RF, DT and Naïve Bayes for activity recognition task, best accuracy is achieved by RF with 75.
B. ANOMALY DETECTION Anomaly detection is the process of detecting unusual behavior in occupancy's normal lifestyle. In  [19] , convLSTM autoencoder is used for anomaly detection, instances with high reconstruction error are assumed to be anomalies because they cannot be reconstructed like normal instances. This is because the autoencoder has been used to train the normal activities only, hence it will be able to do the construction for the normal activities, but, the anomalous activities are not trained, so the reconstruction error is expected to be higher. However, the activities in the dataset have been modified to reflect abnormal behavior related to dementia such as activity repetition, disturbance in sleep and confusion, for instance, activities such as having multiple lunches or forgetting to have dinner and prepare it in irrelevant time are synthesized in the dataset.
Hence, about 150 activities such as ''Eating'', ''Bed_to_toilet'', and ''Sleeping'' are added in  [19]. In  [14] , a method is proposed to artificially produce abnormal activities to reflect typical behaviors of elderly people suffering from dementia. The highest sensitivity is achieved by HMM, while the highest specificity is achieved by LSTM. The technique of adding synthesized data is done because there exists no publicly available dataset on abnormal behaviors for people suffering from dementia. In  [21] , three levels of anomaly detection are proposed based on boxplot outlier analysis such as instances of activities with irregular number of subevents, unusual durations of activities, and irregular frequency of activities performed in a day. H2O autoencoder is used as a binary classifier within each class.
Anomalies detection based on statistical information of activities can be a promising solution since it learns what is normal and abnormal from generating ground truth using the training data. The ground truth is the information that is known to be real or true, provided by direct observation. Since it is difficult to obtain real observed data, boxplot analysis (such as median, maximum, minimum and outliers) can be used to have approximate information about activities such duration, number of active sensors and so on. This technique has been widely used in previous works such as  [21]. In our paper, we have adopted this technique to detect anomalous instances since it doesn't require any modification to the dataset.