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Record W2092573879 · doi:10.1080/19439962.2011.646386

Obesity, Where Is It Driving Us?

2012· article· en· W2092573879 on OpenAlex
Martin Lavallière, Grant Handrigan, Normand Teasdale, Philippe Corbeil

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

VenueJournal of Transportation Safety & Security · 2012
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversité Laval
FundersWorld Health Organization
KeywordsObesityHuman factors and ergonomicsInjury preventionPoison controlRisk analysis (engineering)MedicineReflection (computer programming)PsychologyApplied psychologyComputer scienceEnvironmental health

Abstract

fetched live from OpenAlex

Obesity is recognized as an important issue that has an impact on several areas of our daily lives, such as driving. In the literature there exists an association between obesity and motor vehicle crashes. The goal of this article is to promote insightful reflection and discussion around this emerging topic. Searches were conducted on Pubmed. Search terms were “obesity” and “driving.” The literature was sorted into a summary of the general ideas and is presented for discussion. Relevant issues discussed include anthromechanical issues and car design, seat belt usage, and obesity-related health complications (ocular pathologies, diabetic complications, and obstructive sleep apnoea/hypopnea). Finally, though limited prevention strategies exist for these issues in the literature, some strategies are presented for consideration. With such a complex issue, there is no simple solution. Education is the first step, and with a comprehensive understanding of the risks, actions can be taken to prevent these issues.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.317
Teacher spread0.298 · 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