Adapting quantitative tools to support the assessment and management of wild Pacific salmon in an era of legislative, environmental, and socio-political change
Bibliographic record
Abstract
Status assessments under Canada’s Wild Salmon Policy (WSP) were developed to meet objectives of maintaining biodiversity at the scale of Conservation Units, CUs, which are groups of wild salmon that cannot easily recolonize if lost because of unique adaptations. However, new legislative requirements in Canada under the Fisheries Act have led to the development of “Stock Management Units” (SMUs) for Pacific salmon that often require assessment at larger scales relevant for fisheries management. Standard approaches of aggregating information across units by summing abundances risk failure to achieve WSP biodiversity objectives. We describe alternative approaches for defining reference points and providing management-oriented advice at the SMU level that are consistent with objectives related to CU biodiversity and can more explicitly integrate risks associated with environmental change. In an era of sociopolitical change, we further provide visions for the future of inclusive, collaborative approaches to assessing status that bridge a plurality of knowledge systems. These approaches can be supported by new metrics that reflect spatial distribution, diversity at various scales, and the contributions of salmon to the ecosystem.
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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 | 0.046 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".