Hazard Identification and Risk Assessment for Improving Farm Safety on Canadian Farms
Bibliographic record
Abstract
Agriculture is one of the most hazardous industries worldwide. The number of serious accidents on farms, despite sophisticated technology, development of effective prevention methods, and high-quality training and improved skill levels of farmers, is still very high. The purpose of this study was to develop and apply a generic farm safety protocol to hazards that have been identified in previously published literature and demonstrate the potential benefits of such a protocol with a view to raising awareness of farm safety. Hazards in agriculture were categorized, and literature highlighting the risks associated with hazards was collated. A protocol was developed and applied to establish the likelihood of a hazard causing injury and the consequence of that injury should adverse effects of hazards be realized. The results indicated farm ownership, farm being used as a primary residence, and missing rollover protective structures as the greatest farm risks with expected likelihood and extreme consequence such as death or permanent disablement. Other hazards that require immediate attention while developing mitigation strategies include accident history and existing medical conditions of the farmer, working environment (i.e., alone and isolated), water bodies in the proximity of the farm, lack of periodic machine maintenance, uncovered power take-off and other rotating parts of the tractor, missing safety decals, auger entanglements, and unprotected use of pesticides. Intervention strategies may be guided by considering the results presented in this study. Moreover, farm safety specialists should increase their efforts to promote effective injury prevention methods and enforce safe work environments. The developed protocol addresses almost all common aspects of farming hazards and can be used to mitigate risks associated with hazards in any farm setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".