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Record W2139897526 · doi:10.1186/1479-5868-10-66

The potential modal shift and health benefits of implementing a public bicycle share program in Montreal, Canada

2013· article· en· W2139897526 on OpenAlexafffundabout
Daniel Fuller, Lise Gauvin, Yan Kestens, Patrick Morency, Louis Drouin

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsCentre Hospitalier de l’Université de MontréalSaskatchewan Health AuthorityUniversité de MontréalUniversity of SaskatchewanSaskatchewan Health
FundersCanadian Institutes of Health Research
KeywordsModal shiftPublic transportTRIPS architecturePublic healthPromotion (chess)Sample (material)Health promotionBehavioural sciencesTransport engineeringPoison controlPsychologyEnvironmental healthGerontologyMedicineEngineeringPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: This study estimated the modal shift associated with the implementation of a public bicycle share program in Montreal, Canada. METHODS: A population-based sample of adults participated in two cross sectional telephone surveys. Self-reported travel behaviors were collected at the end of the first (fall 2009) and second (fall 2010) season of implementation. The sample included 2502 (Mean age=47.8 years, 61.8% female), and 2509 (Mean age=48.9 years, 59.0% female) adult respondents in each survey. RESULTS: The estimated modal shift associated with the implementation of the PBSP from motor vehicle use to walking, cycling, and public transportation was 6483 and 8023 trips in 2009 and 2010. This change represents 0.34% and 0.43% of all motor vehicle trips in Montreal. CONCLUSIONS: The implementation of a PBSP was associated with a shift toward active transportation. The modal shift was complex and not simply the result of a discrete shift from one mode to another. Promotion of active transportation should encourage integration of multiple active transportation modes to better reflect people's actual transportation behaviors.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.413

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 communication0.0000.001
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 head0.042
GPT teacher head0.364
Teacher spread0.322 · 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 teacher head, 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

Citations95
Published2013
Admission routes3
Has abstractyes

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