Analysis of EA as an instrument for wetland protection: insights from the mining sector in western and northern Canada
Bibliographic record
Abstract
Environmental assessment (EA) is a primary tool for identifying and managing the impacts of development on wetlands. Despite the global presence of EA and wetland policies, wetlands continue to be lost. This paper examines how EA is being deployed to identify, assess, and mitigate impacts on wetlands. The focus is on the mining sector in western and northern Canada. A sample of 36 EAs for mining projects in British Columbia and Yukon, filed between 2010 and 2021, was analyzed to examine how wetlands were considered in the project description, baseline, impact analysis, mitigation, and management plans. Results indicate a narrow focus on the wetland area as a proxy for impacts and a dominant focus on direct impacts, indicating that the full extent of potential impacts on wetland functions is not captured; a tendency to prioritize mitigation of impacts on habitat with less attention to other wetland functions; a reliance on secondary sources versus field-based studies to identify impacts; and weak linkages between mitigations and impact predictions. Lessons emerging emphasize the need for improvements to foundational EA practices for wetland assessment and mitigation. Better practice is urgent considering the declining state of wetlands globally, coupled with an expanding mining sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".