Quantifying the impacts of oil sands development on wildlife: perspectives from impact assessments
Bibliographic record
Abstract
Anthropogenic landscape disturbances, including industrial development, can have significant impacts on wildlife populations. In Canada, federal, territorial, and provincial laws require major industrial development projects to submit detailed environmental impact assessments (EIA) reports as part of the project application process. These assessments are meant to establish baseline habitat conditions and predict which landscape components will be altered by the project and to what degree. Based on these changes, indirect predictions for wildlife impacts are made using a variety of models, which can vary in validation adequacy and often rely heavily on expert opinion. In the oil sands region of Canada, wildlife species and habitat types used to make predictions are not comprehensive nor standardized between EIAs, despite a high degree of landscape similarity between projects. We extracted habitat model parameters, projected impacts, and anticipated mitigation effectiveness from 30 project EIAs. Despite all these projects occurring in the same natural region, we found very little agreement in the species used to assess wildlife impacts as well as the parameters used to model impacts on those species. Relative to unvalidated habitat models, we found that models receiving independent validation required half the habitat amount for proponents to conclude that the project will have an adverse effect. Our analyses have exposed many areas where policy could improve the efficiency of the EIA process as well as the scientific rigour underlying regulatory decisions.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".