Analysis Of An Accident In The Mining Sector Using The Feyer and Williamson Method
Bibliographic record
Abstract
This research presents the case of the analysis of an accident in an aggregate processing plant through the Feyer & Williamson method [1,2].This method was designed to allow the coding of a time sequence of up to 3 events that have preceded a given accident.These events are called Preceding Events and are characterised as determining factors for the genesis of the accident.In addition, causal factors can also be identified, which are considered to have influenced the accident but not in such a decisive way as the events.The method makes it possible to identify 4 types of events and 8 types of causal factors, as well as different types of human error that have directly influenced the origin of the accident.Results: Once all the causes and factors that directly or indirectly influence the origin of the accident analysed according to the method indicated have been identified and classified, all of them are organised graphically, which allows a quick and simple understanding of the circumstances of the accident.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".