The future direction of pit lakes: part 1, Research needs
Bibliographic record
Abstract
Abstract Pit lakes are common features of open pit mining and can present significant risks, and yet can also provide beneficial end use opportunities. Many processes that influence the magnitude of these risks and opportunities remains poorly understood, which presents a challenge to pit lake closure and management. In this two-part manuscript, four pit lake subject matter experts from Germany, Canada, Australia, and the USA recommend focus areas for researchers (Part 1) and strategies to structurally improve the practice of pit lake closure for mining industry regulators and corporate sustainability officers (Part 2). In this Part 1, we recommend nine research areas, organized by order of physico-chemical and ecological complexity, where greater understanding of fundamental pit lake processes would lead to improved pit lake management and reuse. Our intent is to guide the direction of emerging and future pit lake research by academic and industry research teams, with funding and oversight from industry and government.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".