Operational Characteristics of Tractors Driven by Children on Farms in the United States and Canada
Bibliographic record
Abstract
Farm tractors are an important source of traumatic injury for children on farms. There is, however, no documentation about the age and size of tractors that children are operating and little information about the frequency with which rollover protective structures (ROPS) are used. This study described tractors that children on farms in the U.S. and Canada were operating by age, horsepower, and the presence of ROPS, according to the age and gender of the farm children involved. As a sub-analysis of data compiled during a randomized controlled trial, a descriptive analysis was completed on work exposure data collected by telephone interview. Of the 1,113 children involved in the trial, 522 (47%) were reported to perform at least one job that involved the operation of a farm tractor, and 408 (36.7%) were operating tractors of at least 20 horsepower. The majority of these children were male. There was a wide range of ages and sizes of tractors operated. However, the majority of tractors were between 20 and 70 horsepower and manufactured after 1970. Nearly one-half of the tractors were equipped with ROPS, and these tended to be newer and larger tractors. This analysis provides new data about the broad range of tractors driven by farm children in the U.S. and Canada. The findings point to a need to re-examine the reliance on a single voluntary standard to mitigate the hazard of tractor rollovers and the need for an enhanced safety policy requiring all tractors operated by children be equipped with ROPS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".