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Record W3086942951 · doi:10.24095/hpcdp.40.9.04

The Canadian Bikeway Comfort and Safety (Can-BICS) Classification System: A common naming convention for cycling infrastructure

2020· article· en· W3086942951 on OpenAlexafffundvenueabout
Meghan Winters, Moreno Zanotto, Gregory Butler

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsPublic Health Agency of CanadaSimon Fraser University
FundersMichael Smith Health Research BCPublic Health AgencyPublic Health Agency of Canada
KeywordsAlcohol consumptionEnvironmental healthMedicineHumanitiesAlcoholArt

Abstract

fetched live from OpenAlex

INTRODUCTION: Reporting on alcohol use among women of reproductive age in Canada addresses a major gap in evidence. METHODS: We assessed the prevalence of weekly and heavy alcohol consumption among women aged 15 to 54 years by sociodemographic characteristics, province of residence and concurrent use of other substance(s) using data from the 2019 Canadian Community Health Survey. RESULTS: Of the target population, 30.5% reported weekly and 18.3% reported heavy alcohol consumption in the past year. Prevalence varied by sociodemographic characteristics, province and substance use. The most notable and significant differences were to do with cannabis use and smoking. CONCLUSION: This information can guide health care providers in assessing alcohol consumption and in promoting low-risk alcohol drinking to prevent alcohol exposure during pregnancy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.055
GPT teacher head0.380
Teacher spread0.325 · 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.

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

Citations42
Published2020
Admission routes4
Has abstractyes

Explore more

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