Mine waste management in wet, mountainous terrain : some British Columbia perspectives, part II : creating, managing and judging our legacy
Bibliographic record
Abstract
The mining industry in British Columbia is in the process of constructing a number of large mine waste impoundments contained by large dams, and given robust conditions within the industry, more such facilities are being planned. In many instances, these impoundments are required to maintain a state of permanent submergence to prevent acid rock drainage from the impounded tailings and waste rock. Much has been learned in how to properly characterize and manage these wastes, and how to construct the dams required to contain and flood them. British Columbia has made substantial contributions to this body of knowledge, experience, and evolving practice. What has been learned and incorporated into the construction of these mine waste impoundments will be the mining industry’s bequest to future generations. The principles of sustainability mandate that we consider fully, today and in the future, the nature of that bequest, how best to manage it, and how it is likely to come to be viewed, and managed, by future generations, for there is never an inopportune moment to step back and contemplate what we are doing today and planning for tomorrow. [All papers were considered for technical and language appropriateness by the organizing committee.]
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".