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Record W2977322129

Cycling in Canada.

2017· article· en· W2977322129 on OpenAlexaffabout
Pamela L Ramage-Morin

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsCyclingDemographyGerontologyOccupational safety and healthInjury preventionSuicide preventionNational Health Interview SurveyMedicinePopulationPoison controlGeographyEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

This article provides an overview of cycling, including fatalities and bicycle helmet use, based on data from the 1994/1995 National Population Health Survey, the 2013/2014 Canadian Community Health Survey and the Vital Statistics-Death Database. In 2013/2014, an estimated 7.0 million Canadians aged 12 or older (24%) reported cycling in the previous three months, compared with 6.5 million (29%) in 1994/95. The prevalence of cycling declined with age, and was more common among males and people living in higher-income and -education households. From 1994 through 2012, 1,408 cyclists were killed, an average of 74 annually, most of whom were male (84%). In 2013/2014, 45% of those who had cycled in the past three months reported that they always wore a helmet.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.059
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.007
Science and technology studies0.0050.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0590.009

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 head0.043
GPT teacher head0.274
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2017
Admission routes2
Has abstractyes

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