RISK FACTORS FOR FATALITIES IN SMALL UNDERGROUND COAL MINES IN THE U.S.
Bibliographic record
Abstract
Background The rate of fatal injuries for small underground coal mines (≤20 employees) has consistently been greater than the rate at larger mines. Despite differences between small and large mine characteristics, questions remain regarding reasons for the fatality differences. Objective Explore whether small mines operated more intermittently than large mines, to determine if operational instability contributes to fatalities. Methods A case control study using data from the U.S. Mine Safety and Health Administration from 1983 to 2006. Case mines (fatality; n = 139) were identified by the quarter in which a fatality occurred. Control mines (no fatality) were randomly selected (5:1), without replacement, and were matched with case mines if they were operating during the quarter in which the fatality occurred. Student's matched t-test was used to analyze differences between cases and controls for each of the four quarters prior to the fatality for key factors. Matched logistic regression analyses were also used. Results There were no significant differences between cases and controls in the number of employees, hours per employee, or productivity for any of the four quarters prior to the fatality. Recent periods of low productivity, followed by the addition of new workers and an increase in hours worked, were found to be associated with an increased risk of fatality. Significance Results support the implications of a sudden increase in workforce size, overtime, and workload, presumably, to respond to increased needs for coal. Prevention strategies and policies are needed to address risks from operational instability and excess production.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".