<scp>T</scp>oward cumulative effects assessment and management in the Athabasca watershed, Alberta, Canada
Bibliographic record
Abstract
Abstract This article examines watershed cumulative effects assessment and management (CEAM) in the Athabasca watershed, Alberta, Canada. Using a focus group and semi‐structured interviews with 30 key informants from government, industry, NGOs, and First Nations, watershed CEAM was examined based on eight requisites to support CEAM: the presence of a lead agency; enabling legislation; financial and human resources; data management and coordination; multi‐scaled monitoring; CEAM baselines, indicators, and thresholds; multi‐stakeholder collaboration; and vertical and horizontal linkages. Results show that while there was broad agreement amongst participants concerning the necessity for these requisites, there was also considerable uncertainty respecting these requisite performances in this watershed. Several contributing factors may help explain this uncertainty. Participants noted a lack of willingness to share data to support CEAM, especially spatial data, as well as a lack of confidence in the integrity of water monitoring data. An absence of coordination and leadership for watershed CEAM has contributed to financial, human, and technical capacity limitations as well as power asymmetries respecting multi‐stakeholder engagement. Our results suggest that notwithstanding investment in cumulative effects science and monitoring in the Athabasca, advancing watershed CEAM requires much greater attention to the institutional requisites to implement and sustain CEAM programs.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 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; 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".