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Economic Burden of Agricultural Machinery Injuries in Ontario, 1985 to 1996

2003· article· en· W1981390495 on OpenAlexaffabout
Alison R. Locker, John Dorland, Lisa Hartling, William Pickett

Bibliographic record

VenueThe Journal of Rural Health · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsQueen's University
Fundersnot available
KeywordsAgriculturePrioritizationOccupational safety and healthEconomic impact analysisPoison controlInjury preventionAccidentalEconomic costActivity-based costingEnvironmental healthIndirect costsBusinessMedicineAgricultural economicsGeographyEngineeringEconomics

Abstract

fetched live from OpenAlex

CONTEXT: Agricultural injuries are an important and understudied category of occupational injuries. PURPOSE: This study estimated the economic burden of agricultural machinery injuries that occurred in Ontario, Canada's largest province, between 1985 and 1996. METHODS: Conventional methodology for estimating economic burden, as embodied in a computer program previously developed for this purpose, was applied to hospitalized, nonhospitalized, and fatal agricultural machinery injuries. FINDINGS: The total economic burden of these injuries over the 12-year study period was estimated to be 228.1 million dollars, or 19.0 million dollars annually (1995 Canadian dollars, 3.0% discount rate). By extrapolation, the economic burden of all farm injuries in Canada is estimated to be between 200 and 300 million dollars annually. CONCLUSIONS: Costing information about agricultural injuries provides support for the prioritization and development of injury-control initiatives.

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)0.001
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 score0.443
Threshold uncertainty score0.920

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 communication0.0000.000
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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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

Citations15
Published2003
Admission routes2
Has abstractyes

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