Environmental sustainability, decision-making, and management for mineral development in the Canadian Arctic
Bibliographic record
Abstract
The Canadian Arctic is a complex and fragile region which is currently experiencing unprecedented environmental degradation due to climate change. The effects of climate change on the Canadian Arctic is just one example where we’re seeing the decline of ecosystems and socio-culture-environmental traditions. Mineral development in this already fragile ecosystem is indeed a contentious and high-risk endeavour. However, mining is currently one of the few industries and economic development opportunities in the Canadian Arctic, which is one of the poorest regions in Canada. Unfortunately, mining struggles to achieve environmental sustainability due to mineral development’s inherent trade-off of short-term economic gains for long-term environmental impacts. Local communities are usually left with trying to find this balance. This paper analyzes how we can apply decision-making techniques and environmental management tools for the Canadian Arctic’s mining industry to promote better environmental sustainability, understanding of environmental-economic trade-offs, and community involvement. Specific decision-making methodologies and management tools are analyzed to develop, discuss, and explore their application for the Canadian Arctic. This paper concludes with a framework that brings together the analyzed methods and Arctic specificities; to prioritize environmental issues and to ensure long-term thriving communities in the Arctic.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".