Using Fault Tree Analysis to Identify Contributing Factors to Engulfment in Flowing Grain in On-Farm Grain Bins
Bibliographic record
Abstract
Findings reported by researchers at Illinois State University and Purdue University indicated that since 1980, an average of eight individuals per year have become engulfed and died in farm grain bins in the U.S. and Canada and that all these deaths are significant because they are believed to be preventable. During a recent effort to develop intervention strategies and recommendations for an ASAE farm grain bin safety standard, fault tree analysis (FTA) was utilized to identify contributing factors to engulfments in grain stored in on-farm grain bins. FTA diagrams provided a spatial perspective of the circumstances that occurred prior to engulfment incidents, a perspective never before presented in other hazard analyses. The FTA also demonstrated relationships and interrelationships of the contributing factors. FTA is a useful tool that should be applied more often in agricultural incident investigations to assist in the more complete understanding of the problem studied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".