Perspectives from landscape ecology can improve environmental impact assessment
Bibliographic record
Abstract
The outcomes of environmental impact assessment (EIA) influence millions of hectares of land and can be a contentious process. A vital aspect of an EIA process is consideration of the accumulation of impacts from multiple activities and stressors through a cumulative effects assessment (CEA). An opportunity exists to improve the rigor and utility of CEA and EIA by incorporating core scientific principles of landscape ecology into EIA. With examples from a Canadian context, we explore realistic hypothetical situations demonstrating how integration of core scientific principles could impact EIA outcomes. First, we demonstrate how changing the spatial extent of EIA boundaries can misrepresent cumulative impacts via the exclusion or inclusion of surrounding natural resource development projects. Second, we use network analysis to show how even a seemingly small, localized development project can disrupt regional habitat connectivity. Lastly, we explore the benefits of using long-term historical remote sensing products. Because these approaches are straightforward to implement using publicly available data, they provide sensible opportunities to improve EIA and enhance the monitoring of natural resource development activities in Canada and elsewhere.
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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) | 0.041 | 0.001 |
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; both teacher heads agree on what is shown here.
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".