Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
Abstract.—As part of the Sustainability of the Arctic-Yukon-Kuskokwim (AYK) Salmon Fisheries Symposium held in Anchorage, Alaska during February 5–9, 2007, we identify and discuss the issues that connect the symposium’s presentations and papers (this volume), especially those focusing on AYK stock status and management. We also describe themes that, based on our experience, we know exist but were not explicitly discussed by symposium participants. Issues emerging from the symposium included: different mechanisms affecting stock abundance, cooperation between industry and management, the challenges of stock-recruit curves, effects of broad-scale environmental instability, biocomplexity and stock-specific management, management power-sharing, and conflicts among fisheries. Two unspoken themes were (1) the management tensions between up-river and down-river fishers on the Yukon and Kuskokwim rivers and between state and federal management authorities, and (2) data sharing among the scientific, management, and stakeholder communities. Understanding how escapement goals were set, and identifying where shared values exist among stakeholder groups and managers were themes that should have gained attention and discussion. Each of the themes could provide a focus for future discussion, research, and resolution, thus leading to alternative ways to manage salmon in the region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".