ADVANCING WATERSHED CUMULATIVE EFFECTS ASSESSMENT AND MANAGEMENT: LESSONS FROM THE SOUTH SASKATCHEWAN RIVER WATERSHED, CANADA
Bibliographic record
Abstract
Watersheds are under increasing pressures from the cumulative environmental effects of human actions. Reviews of recent practice suggest that cumulative effects assessment and management (CEAM) has failed to capture the full range of stressors to Canada's watersheds. Indeed, the limitations to CEAM have been well documented; yet, there has been limited constructive evaluation to help explain why CEAM has failed to advance. In this paper we examine the underlying challenges to the assessment and management of cumulative effects in a watershed context. Based on lessons emerging from the South Saskatchewan watershed, Canada, challenges to CEAM in watersheds include stakeholder understanding and interpretation of cumulative effects; limitations in the scale of current EA practices in watersheds; data challenges; the lack of established thresholds for watershed effects; and lack of clarity regarding watershed science and regulatory capacity. The merits of a watershed-based approach to CEAM to help address these challenges to practice are explored.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".