Bibliographic record
Abstract
The global mining community has seen a dangerous sequence of failures in tailings dams, beginning with Mount Polley mine, followed by the Samarco, Cadia Valley and Córrego do Feijão mines. This sequence of failures began on August 4, 2014, at the Mount Polley tailings storage facility in British Columbia, Canada. The initial failure in the embankment at the Mount Polley tailings storage facility had substantial impact on the global mining industry. The Independent Expert Engineering Investigation and Review Panel (IEEIRP) tasked with the investigation of the breach in the tailings dam at Mount Polley made major contributions for new guidelines. The incident has given rise to comprehensive recommendations for best available tailings technologies (BAT) based on principles such as the elimination of surface water from impoundments with the promotion of unsaturated conditions in the tailings through drainage provisions. The application of these BAT principles for the surface storage of tailings leads to the use of filtered tailings technology. Filtered tailings technology or “dry stack tailings” can satisfy each of the BAT components when the impoundment is properly designed and constructed. The implementation of the best available technologies for the physical stability (BAT-PS) of tailings impoundments competes directly with the best available technologies for the chemical stability (BAT-CS) of reactive tailings that may produce acid and metalliferous drainage. The new expertise in mine waste management required to achieve both BAT-PS and BAT-CS are discussed in the present paper.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".