Examination of recent hydroelectric dam projects in Canada for alignment of baseline studies, predictive modeling, and postdevelopment monitoring phases of aquatic environmental impact assessments
Bibliographic record
Abstract
Environmental impact assessment (EIA) has been widely criticized by the aquatic science community for poorly aligned approaches when selecting endpoints and collecting data during the baseline, predictive modeling, and postdevelopment monitoring phases. If these critical phases of the EIA process are not aligned properly, it can be difficult to evaluate the presence of postdevelopment effects. Examples of the misalignment of these phases include baseline studies failing to measure indicators that are monitored postdevelopment; predictive assessments that do not quantitatively predict conditions or potential impacts postdevelopment; and the failure to identify relevant indicators that may detect effects postdevelopment. For aquatic assessments, understanding how to protect critical ecosystem attributes to satisfy regulatory concerns could help to better align aquatic science monitoring activities across EIA phases. In this article we investigate recent Canadian hydroelectric dam EIAs to evaluate how well recent assessment approaches are meeting these necessary conditions of good aquatic EIA practice through the lens of ecosystem services from a fish's perspective. We found that larger facilities generally had baseline studies and modeling that better supported postdevelopment monitoring, but improvements in structure, linkages, and expectations would better align EIA phases in a manner that would improve assessments and environmental protection. Integr Environ Assess Manag 2024;20:616-644. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".