Development of a site-specific, relative hazard prioritization tool at a legacy mine district in British Columbia
Bibliographic record
Abstract
Legacy underground historical mining sites near and around urban areas may include the presence of unsecured openings to surface (surface features), that pose a risk to public safety.These historically mature mining camps (e.g., over 100 years old) operated during previous social circumstances that led to towns being developed around the mines, and the population was more aware of the associated risks of being adjacent to an active mine.Long after the mines ceased operating, corporate mergers and land transfers occurred, new populations less connected to the mining legacy arrived and the current mine site owners face a challenge managing both real, and perceived risks.For a specific legacy mine site in British Columbia, Canada, active management of over 400 mapped surface mining features required the development of a tool to assist the owner with prioritization of ongoing investigation, monitoring and mitigation efforts.The objective of the site-specific, relative hazard prioritization tool was to apply a systematic and consistent approach to assist with relative prioritization of surface hazards to support mitigation effort decisions and the frequency of monitoring of unmitigated features.The tool was designed to use qualitative feature traits based on the geotechnical hazard consultant and owner's combined experience on the project site to characterize the relative geotechnical hazard of each surface feature.This paper presents the process used to develop the tool, how it has been applied on the project site, and its potential application to other sites within the owner's portfolio of legacy underground mining sites.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".