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Motor Vehicle Collisions and Their Demographics: A 5‐Year Retrospective Study of the Hamilton‐Wentworth Niagara Region*

2008· article· en· W2145733911 on OpenAlexaff
Carolyne E. Lemieux, John Fernandes

Bibliographic record

VenueJournal of Forensic Sciences · 2008
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsHamilton Regional Laboratory Medicine ProgramMcMaster University
Fundersnot available
KeywordsDemographicsRetrospective cohort studyPopulationInjury preventionPoison controlTruckMedicineDemographyCannabisMotor vehicle crashOccupational safety and healthGeographyEnvironmental healthForensic engineeringEngineeringSurgeryPsychiatry

Abstract

fetched live from OpenAlex

This retrospective study examined population demographics associated with motor vehicle collision (MVC) fatalities over a 5-year period in the Hamilton-Wentworth Niagara region. Variables were drawn from the five factors proposed by Fierro (1) for investigating deaths caused by transportation: human, chemical, environmental, vehicular, and highway. Factors analyzed included age, gender, position to the vehicle, site(s) of injury, toxicology, environmental contributors, and vehicular findings. From 1999 to 2004, there were 321 MVC fatalities that primarily involved males 20 to 29 years of age and commonly drivers or pedestrians. Cars and trucks were the most frequent vehicles. Fatalities occurred most often on local and regional roads on Fridays and Sundays between 6 pm and 6 am. Mechanical failure and weather conditions were not significant contributors. Toxicological analyses (275/321) were performed on the majority of the study population. Ethanol was present in isolation and with other substances, especially cannabis, mostly in male drivers 20-59 years of age.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.212
Teacher spread0.194 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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