Identifying Optimal Sites for Aquaculture in Southern Ontario Aggregate Pit Lakes: A Sustainable Post-Mining Land-Use Alternative
Bibliographic record
Abstract
Approximately 20%, or 967 aggregate extraction operations in southern Ontario, currently operate below the water table. As these sites transition into pit lakes, they are often characterized by low nutrients, steep walls, and limited biological activity. These future pit lakes represent a unique opportunity for repurposing to create positive social and economic impacts. However, effective post-mining land-use (PMLU) in pit lakes must carefully consider the limnology, geography, and technical attributes of each site. We propose the implementation of aquaculture as an innovative and sustainable PMLU strategy in pit lakes. With proper management, aquaculture creates a positive environmental, economic and social impact by adding nutrients to the oligotrophic lake, generating jobs and producing new regional food sources. Currently, there are no standardized methods or criteria to assess pit lakes for aquaculture suitability. Similarly, no comprehensive public database of pit lakes exists in Ontario. As such, we synthesize knowledge from scientific literature, historical cases of aquaculture and conducted interviews with experts to summarize the aquaculture success criteria. Then, we developed a GIS database for aggregate pit lakes in southern Ontario that includes social, technical and economic indicators derived from publicly available data. Finally, to further facilitate the decision-making processes, we developed a pre-screening model that assigns compatibility scores to each site. Our pre-screening model leverages a multi-criteria decision analysis methodology using 6 proxy indicators such as lake surface area, thermal profile and local community vulnerability indicators. Our model aggregates this data and then assigns scores to each pit lake using both the Simple Additive Weighting (SAW) and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) multi-criteria decision analysis. According to our model, multiple sites in our 186-pit lake database demonstrate the fundamental characteristics for suitability to aquaculture. Our comprehensive research, data solutions and pre-screening tool facilitate the development of sustainable and innovative pit lake rehabilitation techniques. Future research will further expand our scope beyond southern Ontario and assess other PMLU opportunities associated with valuable mine waters.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".