Bibliographic record
Abstract
Abstract Tailings dam failures have occurred both in countries with strict standards in tailings disposal and in countries with insufficient regulation of this activity. Indeed, it is possible to mention catastrophic failures of tailings dams in Chile (in 1928, 1965 and 1985), Guyana (Omai in 1995), Spain (Los Frailes in 1998), Canada (Mount Polley in 2014), Australia (Cadia in 2018) and Brazil (Samarco in 2015 and Brumadinho in 2019). The release of tailings and the consequent downstream flow has caused severe environmental damages and, in many cases, loss of human life. The observed systematic increase in tailings dam failures is unacceptable to both society and the mining industry. The current mismanagement of tailings dams has generated strong reactions from various organisations and led to demands for significant improvements in the design, analysis, construction, operation and closure of tailings dams. Consequently, this chapter attempts to provide the fundamental geotechnical concepts that are crucial in the seismic stability of tailings dams.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".