Summary of Fatal Entrapments in OnâFarm Grain Storage Bins, 1966â1998
Bibliographic record
Abstract
For over 30 years, Purdue University has maintained a national database of agriculture-related entrapment cases that have occurred in loose agricultural material. At present, 391 documented fatal and non-fatal entrapments from the U.S. and Canada make up the Purdue University Agricultural Entrapment Database. In order to specifically study fatal cases of entrapments in grain bins located on farms, the database was reviewed, 181 cases were identified using specific criteria, and the results were summarized. Approximately five cases per year were identified between 1966 and 1998, representing 18 states and one Canadian province. Entrapments were generally reported more often in the top corn-producing states and during the months of November, December, January, March, and June. In 24% of the cases in which the victim's age was known, the victims were younger than 16. Children and adolescents younger than 16 were more often fatally entrapped in June than in any other month. For cases in which the product was known, corn was the agent of injury in 53% of the cases and was frequently found to be out-of-condition. At the time of entrapment, victims were involved with bin unloading activities in 76% of the cases in which the activity was identified. These findings are being used to design new injury prevention strategies, including educational materials and recommendations for engineering controls that focus on primary causative factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".