Continued evolution of mine closure practices: integration of Indigenous perspectives and climate change in revegetation prescriptions
Bibliographic record
Abstract
Climate change is having an impact on weather patterns worldwide. Predicted changes in average conditions and an increase in extreme weather events are expected to have measurable effects on existing ecosystems. Current climate change models, particularly at northern latitudes, indicate the expansion of more moderate ecosystem types, and the potential for entirely new ecosystem types to develop. The success of mine reclamation is conventionally measured against baseline information or historical conditions. However, given the effects of climate change, closure planning and development of associated success criteria solely based on historical weather and ecosystem data and expectations, may be a prescription for failure. To ensure successful post-mining landscape resilience, new methodologies are being developed and regulatory bodies, such as the British Columbia Ministry of Mining and Critical Minerals, are encouraging incorporation of climate change considerations in mine closure and returning land use planning. Recent ecosystems planning methods for reclamation employ the use of a climate-shifted baseline to develop metrics for evaluating closure success and include evolving success criteria based on lessons learnt and the ability to integrate improved climate knowledge, models, and predictions. ERM Consultants Canada (ERM) has been exploring climate modelling and climate change informed species selection tools, and multi-phased planting strategies in land reclamation. The success of these methods is underpinned by the need to align and collaborate with Indigenous nations on their intended future use of the land. By aligning what is technically feasible on post-closure landscapes from a climate change perspective with the cultural values and traditional uses, vegetation prescriptions can be designed to ensure post-closure success in a changing climate. This paper explores these best practices, and identifies ways that the industry can adapt, combine, and iterate these practices to improve reclamation success in northern latitudes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".