dataesr/scientific-paragraphs-categorization huggingface dataset:
The impact of this so-called ''open world'' classification problem has been measured for plant species identification in Go€ eau and colleagues  32  and Joly and colleagues. 33 oreover, the elements likely to be of most interest to biodiversity researchers, such as the representation of native or nonnative established (i. e. , spontaneously occurring) taxa in the dataset, were strongly context-dependent, with a far higher proportion of photos of spontaneous plants in the rural setting of the Peak District than in London (Figure  5 ). The London sample was dominated by human-introduced occurrences of nonnatives that were likely to be garden plants, or even indoor shots, such as cut flowers or other decorations. Pilot studies, such as that presented here, are therefore likely to be essential before assuming that collections of images can be used to directly address any given question of interest, even if initial assessments of spatial coverage or tag frequency indicate a rich data source awaiting exploitation.
e. , the plant photographed has been planted or otherwise placed in its photographed location by humans), and a larger proportion of nonnative species (the combination of neophytes, arrived in Britain post-1500, and archaeophytes, arrived pre-1500). or in terms of certain traits, for example, toward species that are typically considered more photogenic due to large colorful flowers or leaves. Search terms could be modified to either focus on a specific sub-group, e. g. , searching using scientific names, or to exclude non-target images, e. g. , excluding images that include the words ''show'' or ''garden'' in their metadata. Finally, high-level image classifiers could be trained to remove images that are clearly not plants, for example, removing images of animals, paintings. High-level classifiers developed to separate images that contain plants from those that do not, without looking to identify species, could be used to find images worthy of further examination in large datasets that do not have metadata (such as titles and descriptions), removing the need for keyword searches, such as that used in this study.
These topics are all suggested as possible uses of Flickr data based on our sampled assessment. Once data are generated by AI naturalists we must consider how they are shared to ensure recognition of their authors, observation traceability, and longterm accessibility. We suggest following the TDWG standards,  43  such as Darwin-Core among others, to ensure the data are interoperable with other biodiversity datasets and can be shared via the GBIF portal (www. gbif. org). Metadata accompanying these datasets should include the AI model architecture, technical specificities of the AI model training phase, digital object identifier of the dataset used to train it, AI model version, classification score of each image, date, location, photograph name, and image license. Classification score, which provides a mathematical value of the confidence in the prediction of a model for a specific image, is particularly important as it can be used in subsequent analyses to filter the data by the level of confidence in the classification accuracy.
A researcher will need to define the appropriate balance to choose the threshold classification score at which to filter these data according to the research question being addressed, as was done in a study of invasive species by Botella and colleagues. 44 An AI classified dataset from Pl@antNet has already been published on GBIF,  45  illustrating the interest of the scientific community in this new type of data. Generalization of our approach to larger geographical scale and other social networks could open the door to a much larger number of automatically identified biodiversity observations on this and other platforms. For any given research question, ecologists and data scientists should carefully consider the steps that might be required to ensure the relevance and accuracy of AI-generated data for any given research question.
Classifiers will tend to be more accurate at higher taxonomic levels, but this may vary between taxonomic groups. 4. What reporting biases exist in your dataset? For example, to what degree are charismatic species over-represented, or nocturnal species under-represented? Can you filter the data, or model the results to account for these biases if they are relevant? 5. Do reporting biases change over space or time? We observed significant differences in reporting bias between urban and rural settings, and we anticipate that temporal biases are likely to exist where public interest in elements of the natural environment change over time. 6. How will you propagate uncertainty in classifications? AI classifications are associated with a classification score which is indicative of the uncertainty in the identification.
This can be used both as a threshold for removing erroneous results, and/or could be included in models to account for variation in uncertainty between observations. 7. Is the dataset used to train your AI naturalist a good match to the images being classified? A poor match between training and prediction datasets will result in higher error rates, which may not always be associated with low classification scores. 8. Have you adequately documented your dataset? To ensure reproducibility and interoperability ensure that you document the model used for classification, filtering steps used to collate images, and other metadata useful to future researchers, and which may be specified in data standards for AI-generated biodiversity which do not exist at the time of writing.
Searching Flickr We accessed publicly visible image data on the website Flickr (www. flickr. com). Flickr is a website used for image hosting and has an application programming interface (API) that allows queries of the image database. Images hosted on Flickr tend to be better annotated with tags, location, and description than other potential sources of image data, such as Twitter, potentially because Flickr is targeted at people with a keen interest in photography. This potentially explains why Flickr has been used in previous studies to develop a better understanding of people's subjective experience of the environment in which they live,  41  and to automate assessment of cultural ecosystem services. 34 We searched Flickr using the R-package ''photosearcher''  46  (https://github. com/ropensci/photosearcher) for images that contained the word ''flower'' in either their title, description, or tags.
50 The network is pre-trained on the commonly used Im-ageNet dataset and fine-tuned on Pl@ntNet data. Pl@ntNet currently covers 30,261 species illustrated by more than 2. 9 million images. The taxonomic coverage of our study is therefore one to three orders of magnitude larger than previously published studies making use of automated species identification for ecological research. The training of Pl@ntNet CNN requires the mobilization of a high-performance computing infrastructure and expertise in deep, distributed, and large-scale learning. Thus, the resulting classification tool is in itself a major advance in biodiversity data science. Access to the Pl@ntNet classification tool is provided through a dedicated API available at my. plantnet. org. The main feature of this API is a RESTful JSON-based web service that can accept one to five images of a plant and returns a list of likely species.
The species are associated with classification scores (the softmax output of the CNN), as well as a list of matching images retrieved from the database. To facilitate the implementation of future studies based on the methodology of this paper, we have developed the ''plantnet'' 51 R-package allowing users to query the Pl@ntNet API. The package is available online at https://github. com/BiologicalRecordsCentre/plantnet. Flickr images were submitted one-by-one to the API, and only the taxonomic identification associated with the highest classification score was retained for each image. No thresholding on the classification score was applied. Only the classification scores and image metadata were stored, Flickr images were not downloaded. A demonstration of the workflow, utilizing the photosearcher  46  and plantnet 51 R-packages, is given in the Supplemental Information.
Expert Assessment Flickr image URLs, metadata (e. g. , geolocations), and Pl@ntNet classification information were stored in CSV files (see Data S1). For each area-London/ Urban or the Peak District/Rural-1,000 (1. 8%) and 100 (1. 8%) random image samples were taken, respectively. An expert botanist (OP, author) subsequently assessed each image, along with its location, other relevant metadata, such as the image title, and the Pl@ntNet prediction, in a web browser using a custom RShiny app (see Supplemental Information). The original Flickr URL of each image was also provided so that the expert could view other contextual information, such as comments on the photo and adjacent images taken by the same photographer. Within the app, the expert assessed each photo using a standard set of questions.
ACKNOWLEDGMENTS An early demonstration of this concept was developed at the  British Ecological Society Quantitative Ecology Special Interest Group hackathon , led by  T. A. Thanks  to members of the hackathon team for their contributions:  Nathan Fox ,  Celia Marlowe ,  Joseph Millard ,  Nadia Bystriakova ,  Elliot Shayle , and  Roy Sanderson. T. A. was supported by  COST  action  CA17122 ''Increasing understanding of alien species through citizen science''  and  Natural Environment Research Council  award number  NE/R016429/1  as part of the  UK-SCAPE program delivering National Capability. We thank three anonymous reviewers for their comments.
Objectives The Observatoire Sommeil de la Fédération de Pneumologie is collecting data in a prospective cohort of patients suspected of having obstructive sleep apnoea  12. Each patient is evaluated during a structured interview associated with an electronic medical record and is then assessed by respiratory polygraphy or polysomnography to confirm the diagnosis of obstructive sleep apnoea and treat it. Apart from the classical symptoms and comorbidities characterizing obstructive sleep apnoea (in particular excessive daytime sleepiness), this national registry includes questions concerning sleepiness at the wheel, the occurrence of near-miss accidents and self-reports of sleepiness-related accidents. The main goal of the current study was to quantify the true value of apnoea + hypopnoea index versus sleepiness at the wheel in order to better identify obstructive sleep apnoea patients at risk for sleepiness-related accidents.
Methods Study design and setting: the OSFP national registry. Data from a prospective national cohort (www. osfp. fr) were used to conduct this study. All participants were offered the opportunity to join the OFSP during a first consultation motivated by a suspicion of obstructive sleep apnoea, based on clinical complaints (snoring, sleepiness…). The OSFP registry  12  is a standardized web-based report administered by the French Federation of Pulmonology. It contains anonymized longitudinal data from patients complaining of sleep disorders who have been investigated by respiratory physicians in private practice, general hospitals and university hospitals. Periodic quality control checks are performed to ensure up-to-standard data-recording. Ethical committee approval for setting up the database was obtained from "Le Comité consultatif sur le traitement de l'information en matière de recherche en santé" (CCTIRS no 09.
Sleep studies. The diagnosis of obstructive sleep apnoea was obtained by full-night polysomnography (PSG) performed at a sleep centre or by polygraphy performed at home. For PSG, sleep stages were scored manually according to the American Academy of Sleep Medicine criteria  9  The scoring of respiratory events was done according to the AASM rules  14. For PG, hypopneas were scored only when associated with a 3% desaturation in oxygen. Apnoea + hypopnoea index was defined as the number of apnoeas and hypopneas per hour of sleep (full-night PSG) or per hour of recording. Variables and data sources. The following data, collected at the first medical visit, were recorded (see supplementary material): demographic characteristics, scores (ESS, Pichot fatigue scale and Pichot depression scale), subjective sleep duration, obstructive sleep apnoea symptoms, office blood pressure, waist circumference and co-morbidities (cardiovascular, metabolic and respiratory).
Statistical methods. Quantitative data were expressed by using median and interquartile range and qualitative data were expressed by using numbers and percentage. A simple imputation method was performed to replace missing data: quantitative variables were imputed using the median and qualitative variables using the most frequent modality. The population was stratified according to obstructive sleep apnoea occurrence and severity (apnoea + hypopnoea index thresholds at 10 and 30 events/h, respectively) and presence or absence of sleepiness at the wheel. Variables were compared between groups using a Chi squared test for qualitative variables and a non-parametric Kruskal-Wallis test for quantitative variables. Post-hoc tests with Bonferroni correction for multiple tests were performed if the overall test was significant.
Statistical analyses were performed using SAS v9. 4 (SAS Institute Inc. , Cary, NC, USA). A p-value < 0. 05 was considered as significant. in the methods section, the population was stratified according to obstructive sleep apnoea occurrence and severity (apnoea + hypopnoea index AHI thresholds at 10 and 30 respectively) and the presence or absence of self-reported sleepiness at the wheel (Fig. 1 ). Tables  1  and 2  present the main characteristics of the subgroups. As expected, male gender, higher BMI and age were associated with severe obstructive sleep apnoea. As shown in Tables  1  and 2 , imputation did not impact the results.
Second, sleepiness at the wheel and sleep-related accidents were collected retroactively, which may introduce a bias in data collection. Even if many other studies on accidents have used similar techniques, there is a need for new prospective studies in order to confirm data obtained until now by studies on accidents due to sleepiness at the wheel. Third, sleepiness at the wheel and sleep-related accidents were collected by self-reports from the patients, so some accidents may have been omitted. No specific time frame was specified during the clinical evaluation, assuming that the symptoms collected reflected the behavioural disability of the patients. However, at baseline evaluation in the sleep clinic, reasons for underreporting accidents are rather limited compared to a context of fitness-to-drive evaluations related to driving licenses clinical consultations.
Acknowledgements The authors are indebted to the OSFP participants who contributed their time to this study. The authors are grateful for the diligent work performed by physicians and the technical staff at the clinical sites for collecting the data. Members of the  OSFP (Observatoire Sommeil de la Fédération de Pneumologie, France) consortium :  Jean Louis Pepin  (nominated consortia representative),  Yves Grillet ,  Marc Sapene ,  Francis Martin  ( Unité des pathologies du sommeil, Centre hospitalier de Compiègne, Compiègne, France ),  Bruno Housset  ( Service de pneumologie et de pathologie professionnelle ,  CHI de Créteil, Créteil, France ),  Hervé Pegliasco  ( Pneumologie, Hôpital Ambroise Pare, Marseille, France ).
Data analysis Calibration factors for each eye were determined from the eye positions during the calibration procedure  (Lions, Bui-Quoc, Wiener-Vacher, Seassau & Bucci, 2013). The software MeyeAnalysis (provided with the eyetracker) was used to extract pursuit eye movements from the data. Detection of saccades during pursuit was based on criteria of minimum amplitude (2°) and velocity (30°/s). Catch-up saccades were defined as saccades in the target direction that served to reduce position error and to bring the eye closer to the target. The number of catchup saccades was counted for each trial. Amplitude was measured for each catch-up saccade. Pursuit gain corresponds to the relationship between the speed of the eye and the speed of the target and it was obtained by dividing eye velocity by target velocity for each trial.
d. ), practicing a team sport; vii) sleep disturbances: global sleep quality (poor/not poor), difficulty in falling asleep/maintaining sleep (more/less than once a week), feeling extremely sleepy during the day (more/less than 3 to 5 times per week), usual lack of sleep with at least one hour less than necessary (more than a few times a week/once a week or less); and viii) substance use: smoking status, alcohol use (more than 4 times a week/3 times a week or less), cannabis use (more/less than once in the previous year), at least one lifetime use of another substance, or at least one use of doping products during competitions or exam periods. The use of other psychotropic medication drugs was not available in the database. The mental health questionnaire consisted of nine psychometric scales: a perceived stress assessment adapted to the student population (17 items based on four main factors: academic stress, dysfunctions at university, feelings of loneliness, and problems with close relations)  (Boujut and Bruchon-Schweitzer, 2009) ; a scale assessing coping strategies in 16 items divided in three parts: search for social support, emotional coping, festive and addictive coping  (Boujut et al.
Statistical method All statistical analyses were performed by using SAS (version 9. 4; SAS Institute Inc, Cary, NC, USA) statistical software. First, we carried out descriptive analyses of the study population. Participants who had also completed the mental health questionnaire were compared with those who did not using univariate tests, the Pearson Chi2 test for qualitative variables and the Student test for quantitative variables. Completers of the follow-up questionnaire were compared with noncompleters. Second, covariates were summarised in disease risk scores (DRS) in order to control for a large number of confounding variables  (Tadrous et al. , 2013). Then, several multivariate logistic regression models were run to assess the association between prescribed anxiolytic/hypnotic drugs and STB.
Study participants This study used data from 7403 people who participated in the 2007 Adult Psychiatric Morbidity Survey (APMS). Full details of the survey have been published elsewhere. 19, 20  to December 2007 using a multistage stratified probability sampling design where the sampling frame consisted of the small user postcode address file, whereas the primary sampling units were postcode sectors. Participant information was obtained through face-to-face interviews where some of the questionnaire items were self-completed (with the use of a computer). Sampling weights were constructed to account for nonresponse and the probability of being selected so that the sample was representative of the English adult household population. The survey response rate was 57%. Ethical permission for the study was obtained from the Royal Free Hospital and Medical School Research Ethics Committee.
Thus, these participants were excluded from the analysis. The scores ranged from 70 to 130 and this variable was analyzed as a continuous variable or as a categorical variable (ie, 70-79, 80-89, 90-99, 100-109, 110-119, and 120-129) in line with a previous publication using the same dataset. 21 rbal IQ is referred to as IQ in this manuscript for the sake of brevity.
Of particular interest is the Kanazawa study that used data from a birth cohort of 17 419 individuals in the United Kingdom and reported an independent effect of childhood F I G U R E 2 Association between intelligence quotient (IQ) and obesity estimated by multivariate logistic regression Note. IQ was assessed using the National Adult Reading Test (NART). Obesity was defined as a body mass index (BMI) of ≥30 kg/m 2. Reference category is IQ 120-129. The model was adjusted for sex, age, ethnicity, marital status, qualification, employment, income, the number of chronic physical conditions, loneliness, social support, the number of stressful life events, smoking status, alcohol dependence, drug use, and common mental disorders. Abbreviations: CI, confidence interval; OR, odds ratio. intelligence on adult obesity, regardless of education or earnings.
In older adults, pronoun processing has been shown to be prone to memory constraints  5, 43. Data from an eye-tracking-during-reading study  44  showed that both young and older adults had increased reading times on mismatching reflexive pronouns, yet older adults compensated for this decline when additional sentential context was provided. A study 7 on a life-span sample of adults (aged 18-81) reported data from self-paced reading experiments addressing interpretation of locally ambiguous reflexive pronouns in complex relative clauses. This study found that both WM span and print exposure predict how pronoun interpretation ability changes in the course of adulthood. There are only a handful of ERP studies on pronoun/syntactic processing in older adults, however, the available research on more broad language-related ERP effects has shown that older adults' ERP components, particularly N400 effects in lexical processing, are delayed and reduced in amplitude (see Wlotko, Lee and Federmeier 45 for reviews).
( Procedures. The sentence stimuli were programmed within a word-by-word moving-window paradigm and presented using the E-Prime 3. 0 software (Psychology Software Tools, Pittsburgh, PA)  62. The participants were seated in front of a presentation monitor within a comfortable reading distance located in a soundproof booth with electromagnetic-shielding. The participants were instructed to read the sentences silently, and at the end of each sentence, to respond to an end-of-sentence acceptability judgement task by pressing (j = 'yes') and (f = 'no') keyboard buttons. The judgement question asked the participants Cette phrase est-elle acceptable? ('Is this sentence acceptable?'), and stayed until the participants provided a response. The sentence stimuli were presented visually word-by-word, each word appeared in the centre of the screen in Courier New font (32 pts.
Breaks were planned between each block in an experiment. The whole experiment took about 50 to 70 min in total (about 2 h including EEG preparation). The participants received 20 Euros as compensation for their time. EEG acquisition and pre-processing. The EEG signals were recorded via a 64-channel (standard 10-20 system) elastic Quick-Cap (Compumedics Inc. ). An additional electrode integrated within the cap (in proximity to Fz) was used as the ground electrode. Horizontal and vertical EOG signals were recorded through bipolar electrodes, and additional two electrodes placed on the mastoids to be used as offline reference were integrated within the cap. The signals were digitally amplified, using the Neuroscan 64-channel EEG system amplifier, at the sampling rate of 1000 Hz (i. e. , one data point per millisecond).
During recording, impedance in all electrode sites was below 10 kΩ. The EEG signals were processed using EEGLAB software toolbox  63  in MATLAB version R2019b. The signals were down-sampled to 256 Hz for computational purposes, and re-referenced to the mastoid electrodes. We applied low-pass and high-pass at the cut-off frequencies of 0. 1-46 Hz. ERPs were segmented for a 2000 ms time frame (400 before and 1600 ms after the later pronoun/referent was presented). In each segmented trial, average data were corrected to -400 ms baseline. Trials with excessive artefacts exceeding 100 μV were semiautomatically detected and rejected. Total amount of rejected trials corresponded to 8. 4% in the young group and 9. 2% in the older group. Data rejection was not greater than 20% of all trials per condition per subject.