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Record W1945358082 · doi:10.5864/d2013-016

Should helmets be mandatory for skiers and snowboarders in Ontario?

2013· article· en· W1945358082 on OpenAlexaffvenueabout
Janet Alsop, Sue Burlatschenko, Sophie Gouveia, Karen Gowdy

Bibliographic record

VenueEnvironmental Health Review · 2013
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLegislationOccupational safety and healthEngineeringNova scotiaPoison controlHuman factors and ergonomicsInjury preventionAeronauticsForensic engineeringBusinessEnvironmental healthMedicinePolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

Every year, approximately 15% of Canadians participate in snow sports. Among skiers and snowboarders, 9%–19% suffer potentially disabling injuries to the head. Case-control studies have shown that ski helmets can reduce the risk of head injury by 29%–60%. Opponents of mandatory ski helmet use have presented a series of arguments against ski helmets. However, numerous studies have demonstrated that ski helmets improve the safety of skiers and represent a benefit. Currently, there is no ski helmet legislation in Ontario. Few jurisdictions have legislation mandating the use of ski helmets. In Canada, only Nova Scotia has legislation regarding the use of ski helmets. In Canada, there are no mandatory safety standards for the manufacturing of ski helmets despite the publication of standards by the Canadian Standards Association in 2008.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.321
Teacher spread0.282 · 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
GenreCommentary

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

Citations3
Published2013
Admission routes3
Has abstractyes

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