Unearthing trends in environmental impact assessments for mines and quarries across Canada
Bibliographic record
Abstract
In Canada, at least 200 active mines and 6500 quarries produce 60 commodities. Many proposed mines and quarries undergo impact assessment (IA) to predict potential impacts and inform final decision-making, alongside conditions for mitigating risk. We (1) present the first complete list of IA laws that apply to mining in Canada; (2) provide an open-source database of mine and quarry projects subject to IA; (3) quantify mining and IA trends; and (4) assess availability of information by jurisdiction. Our database includes 266 assessments of 227 projects under 13 jurisdictions proposed from 1974 to 2023. Over time, target commodities shifted from coal, oil sands, and peat, to metals (e.g., gold, copper, nickel) and production sizes increased. For many projects assessed across multiple jurisdictions, we could not explain differences in reported key metrics (production size, lifespan, footprint). Our database's comprehensiveness is limited by a lack of publicly available data. We recommend regulators adopt the findable, accessible, interoperable, and reusable principles for information sharing by standardizing key metrics, publishing project documentation, and improving registry functionality. We encourage enhanced inter-jurisdictional IA cooperation to ensure the public and decision-makers have access to fulsome information about new projects during a modern mineral rush in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".