The Development and Demonstration of a Semi-Automated Regional Hazard Mapping Tool for Tailings Storage Facility Failures
Bibliographic record
Abstract
Tailings flows resulting from tailings storage facility (TSF) failures can pose major risks to downstream populations, infrastructure and ecosystems, as evidenced by the 2019 Feijão disaster in Brazil. The development of predictive relationships between tailings flow volume and inundation area is a crucial step in risk assessment by enabling the delineation of hazard zones downstream of a TSF site. This study presents a first-order methodology to investigate downstream areas with the potential of being impacted by tailings flows by recalibrating LAHARZ, a Geographic Information System (GIS)-based computer program originally developed for the inundation area mapping of lahars. The updated model, LAHARZ-T, uses empirical equations to predict inundated valley planimetric and cross-sectional areas as a function of the tailings flow volume. A demonstration of a regional application of the LAHARZ-T model is completed for 46 TSFs across Canada. Although the variability in tailings properties and site characteristics cannot be perfectly incorporated or modelled, the LAHARZ-T model offers an efficient method for high-level, regional scale inundation mapping of several potential TSF failure scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".