A new approach to mine accedent analysis : a case study of a mine cave-in
Bibliographic record
Abstract
In May of 1980, a mine operated by Belmoral Mines Ltd experienced a failed crown pillar, resulting in the death of eight miners and putting sixteen others in harmi?½s way. The Ferderber mine was located in the Val di?½Or region of the province of Quebec in Canada, and had been under development for 19 months and production for 9 months, since the opening of the portal. The crown pillar separating the 1i?½7 level from overlying overburden ultimately failed, permitting an estimated 1.5 million cubic feet of liquefied sediment to gain entry into the mine. This paper back-analyses the cave-in by applying decision error theory, in an examination of the organizational culture and the collective decisions contributing to the cave-in disaster. A case will be made for a greater understanding of mining methodology, geomechanics and risk assessment when operating in such challenging geologic conditions. Boundary condition and decision (BCD) analysis will be introduced as an innovative new method for the analysis of mine accidents and incidents. It will also be shown that BCD analysis can be applied to any event in an enterprise for which standards, norms or legislation exists. A cognitive profile will be presented that provides insight into the safety ethos existing within the Ferderber mine organization at the time of the tragedy.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".