Bibliographic record
Abstract
To a large extent, mining is a waste management business. In most mining operations, metal resources are found in host rocks at concentrations of a few percent or less, resulting in the production of large quantities of wastes during metal extraction and processing. These wastes can occur in the form of bulk waste rock, and fine-grained material (tailings) that remain after the ore is ground and processed. The two principal mine-waste management challenges are the containment of tailings, and the management of contamination leaching from tailings and waste rock. Over the past decade, several high-profile, catastrophic tailings-dam failures have led to a significant change in the way mine wastes are treated. New global standards have significantly improved industry tailing-management practices, with the potential to significantly reduce, if not eliminate, the environmental impacts of mine wastes. This essay reviews the complex problem of mine waste management, and discusses emerging new approaches—both technical and regulatory—to help ensure that mine waste storage facilities are safe from catastrophic failure, and non-polluting in perpetuity. More work is needed to ensure that these new approaches become cost-effective so that they can be widely adopted by the global mining sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.042 |
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".