Barriers and opportunities for the effective management of cumulative effects in salmon ecosystems in British Columbia, Canada
Bibliographic record
Abstract
The cumulative effects of climate change and human activities pose major challenges for environmental management, a problem exemplified by Pacific salmon ecosystems. We offer an integrative treatment of both the science and policy levers of cumulative effects and reveal the sheer complexity of effective governance of salmon ecosystems in British Columbia, Canada. We then present and examine a hypothetical conceptualization of cumulative effects and their governance in salmon ecosystems to highlight several barriers and opportunities. We find that the progressive degradation of many salmon habitats appears to be enabled by the current policy approach through scarce monitoring, ineffective assessment, lack of legal limits, and isolated decision-making. At the same time, climate change magnifies the urgency of effective management as human activities act cumulatively with climate change impacts. However, our synthesis also highlights opportunities with existing but underused policy levers within Crown and Indigenous governance, as well as local co-governance arrangements, that could improve salmon ecosystem management. Although positive steps have been made toward managing several stressors, the current challenges facing Pacific salmon underscore the need for a fundamental shift in the treatment of cumulative effects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".