Atmospheric fuzzy risk assessment of confined spaces at mine reclamation sites
Bibliographic record
Abstract
In 2006, a tragic accident took place at the Sullivan mine in Kimberley, British Columbia. Four people died as the result of their entry into an oxygen-depleted sampling station located at the toe of a waste dump. The dump had been in active use for over 50 years and the sampling shed for about 5 years without any problem. The accident was reported as being unprecedented in the history of mining. The accident shows that reclamation sites can be an atmospheric danger only recognizable if a risk assessment is carried out on a regular basis for many years after closure. It is important to conduct regular assessments since there are physical, chemical and environmental factors that affect oxygen-depletion in waste dumps that change over time. In this thesis, an Atmospheric Fuzzy Risk Assessment (AFRA) tool was devised to recognize confined space dangers at sulfide waste dumps undergoing reclamation. The tool is a fuzzy expert system to transfer knowledge on atmospheric hazards. Modeling the complex environment of a waste dump where internal and external factors change temporally and spatially using conventional mathematical tools is a difficult task. Therefore, a technique based on fuzzy logic and weighted inferencing was applied since this method relies on a heuristic approach that allow for case–based reasoning. AFRA can help mining engineers and other safety professionals to recognize this type of danger while developing a confined space inventory at any site. The second goal of this research has been to create an application for hand-held pocket PCs and/or Smart phones that can be used by first-responders to provide answers about a possible confined space situation to help them decide to enter or not into that space.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".