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Record W1993318848 · doi:10.1177/0013916511409033

The Effects of Weather on Walking Rates in Nine Cities

2011· article· en· W1993318848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironment and Behavior · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsPrecipitationEnvironmental sciencePoisson regressionMeteorologySunlightAir temperatureSnowAtmospheric sciencesRegression analysisGeographyStatisticsDemographyMathematics

Abstract

fetched live from OpenAlex

This study examined whether locally felt weather had a measurable effect on the amount of walking occurring in a given locale, by examining the observed walking rate in relation to air temperature, sunlight, and precipitation. Web-based cameras in nine cities were used to collect 6,255 observations over 7 months. Walking volumes and levels of precipitation and sunlight were captured by visual inspection; air temperature was obtained from local meteorological stations. A quasi-Poisson regression model to test the relationship between counts of pedestrians and weather conditions revealed that all three weather variables had significant associations with fluctuations in volumes of pedestrians, when controlling for city and elapsed time. A 5°C increase in temperature was associated with a 14% increase in pedestrians. A shift from snow to dry conditions was associated with an increase of 23%, and a 5% increase in sunlit area was associated with a 2% increase.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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