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Record W2051553949 · doi:10.1177/0013916513510399

Effect of Weather, School Transport, and Perceived Neighborhood Characteristics on Moderate to Vigorous Physical Activity Levels of Adolescents From Two European Cities

2013· article· en· W2051553949 on OpenAlexaff
Alberto Aibar Solana, Julien Bois, Eduardo Generelo, Enrique Garcíá Bengoechea, Thierry Paillard, Javier Zaragoza Casterad

Bibliographic record

VenueEnvironment and Behavior · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcGill University
FundersGobierno de Aragón
KeywordsPhysical activityWalkabilityPrecipitationGeographyPerceptionMultilevel modelPsychologyDemographyEnvironmental scienceMeteorologyMedicinePhysical therapyMathematicsStatistics

Abstract

fetched live from OpenAlex

The main goal of this study is to analyze the influence of several environmental factors (temperature, precipitation, mode and duration of school transport, perception of physical activity [PA] opportunities, and perceived neighborhood walkability) on adolescent’s daily moderate to vigorous physical activity (MVPA) levels of two European mid-sized cities. Data were collected from a sample of 829 adolescents (49.7% Spanish; 55.2% females; 14.33 ± 0.73 years). Daily meteorological data were collected for the valid days for each subject and MVPA levels were assessed with Actigraph GT3X accelerometer during seven consecutive days. Data were analyzed using multilevel modeling. Warmer weather ( p < .01), lower levels of precipitation ( p < .05), and use of active school transport ( p < .05) were significantly associated with higher MVPA levels. Environmental neighborhood perception did not show significant influence. Further efforts should be carried out to increase PA opportunities during colder periods, rainy days, and to promote the use of active transport

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.263
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2013
Admission routes1
Has abstractyes

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