Modelling the transport of tailings after Mount Polley tailings dam failure using multisource geospatial data
Bibliographic record
Abstract
Tailings dams (TD) are usually located in valleys that are not easily accessible and hence one global digital elevation model (DEM) is used in most of the TD breach outflow modelling studies but the challenges this impose when calibrating visco-plastic rheological models has not been fully investigated. Implications of using GeoBase DEMs for model calibration is studied by comparing it with a model that is calibrated using a merged DEM generated from multiple products. The influence of rheological parameters, roughness coefficient and sediment concentration ( Cv) on simulated variables is studied. It was concluded that when GeoBase DEM was used for back analysis of the event, physically meaningful set of rheology values could not be assigned. Flow behaviour was strongly influenced by Cv and least influenced by Manning’s values. Runout distance was more sensitive to viscosity than yield stress. Mudflow depth and arrival time increased with increase in viscosity and Cv.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".