The Development and Demonstration of a Semi-Automated Regional Hazard Mapping Tool for Tailings Storage Facility Failures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it