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Record W2167513374 · doi:10.24095/hpcdp.31.3.03

Patterns of fatal machine rollovers in Canadian agriculture

2011· article· en· W2167513374 on OpenAlexafffundvenueabout
J DeGroot, C Isaacs, William Pickett, RJ Brison

Bibliographic record

VenueChronic diseases and injuries in Canada · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsQueen's UniversityKingston General Hospital
FundersAgriculture and Agri-Food Canada
KeywordsRollover (web design)TowingAeronauticsForensic engineeringEngineeringComputer scienceAutomotive engineering

Abstract

fetched live from OpenAlex

INTRODUCTION: Our objectives were to examine the activities and circumstances associated with agricultural machine-related rollover fatalities. METHODS: We identified agricultural machine rollover fatalities recorded by the Canadian Agricultural Injury Surveillance Program (CAISP) in 1990-2005. We determined sideways and backwards rollovers by year, age and sex of the victims, agricultural season, machine type, and the activity, circumstances and location of the injury event. RESULTS: The annual rate of rollover fatalities in Canada was 9.1 per 100,000 farm operations. Rollover fatalities decreased to 30% of baseline over the 16-year study period (p = .004). Fatal rollovers most often occurred among men aged 50-69 years and 60-79 years for sideways and backwards rollovers, respectively. DISCUSSION: Sideways rollovers occur when driving across an incline or at the edge of a ditch bordering a roadway or field. Backwards rollovers occur when driving up an incline, towing or extracting stuck machines, pulling stumps or trees, and towing implements or logs. Primary prevention programs for rollover injuries should target these identified patterns of injury.

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 score0.014
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.173
Teacher spread0.167 · 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

Citations20
Published2011
Admission routes4
Has abstractyes

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