Transforming open mining pits into fish farms: Moving towards sustainability
Bibliographic record
Abstract
Abstract The legacy of mining activities has typically been land ‘returned to wildlife’, or, at some sites, degraded to such an extent that it is unsuitable for any alternate use. Progress towards sustainability is made when value is added in terms of the ecological, social and economic well‐being of the community. In keeping with the principles of sustainable development, the innovative use of flooded open pits and tailings impoundments as commercial, recreational or ornamental fish farms should be considered in some locations, as it could make a significant contribution to the social equity, economic vitality and environmental integrity of mining communities. This article highlights the growing significance of aquaculture and explores the benefits and barriers to transforming flooded pits and impoundments into aquaculture operations. Among other benefits, aquaculture may provide a much‐needed source of revenue, employment and, in some cases, food to communities impacted by mine closure. Further, aquaculture in a controlled closed environment may be more acceptable to critics of fish farming who are concerned about fish escapes and viral transmissions to wild populations. Despite the potential benefits, aquaculture in flooded pits and impoundments is not without its complications — it requires a site‐specific design approach that must consider issues ranging from metals uptake by fish, to the long‐term viability of the aquatic system as fish habitat, to the overall contribution of aquaculture to sustainability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".