Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
Abstract.—The Arctic-Yukon-Kuskokwim (AYK) Salmon Research and Restoration Program explicitly recognizes the integration of human dimensions with salmon ecosystems. This paper addresses the collaborative management approach to integration by summarizing how collaborative processes work and how they influence management performance. Collaborative fishery management includes stakeholders in a number of management functions such as data collection, research, planning, design, decision-making, monitoring, evaluation, and enforcement. This approach is included in the general category of “co-management,” which refers to the sharing of authority and responsibility among government and stakeholders. Co-management is a process, rather than a tool, of management. The direct involvement of stakeholders in the planning and control of their fisheries offers the potential of improving the performance of fishery management in promoting sustainability. Realizing the potential depends on the extent to which key co-management principles are addressed. These principles relate to three management components: background conditions in the fishery, management structure, and management operations. Background conditions that affect the performance of co-management include uncertainty, history, and context. Elements of fishery structure relating to co-management performance include boundaries, scale, representation, and participation. Fishery management operations influence co-management performance through stability and flexibility, cost effectiveness, and equity. The principles underlie co-management performance through the effect they have on transaction costs and incentives. Columbia River salmon recovery provides a good example of the influence of transaction costs and uncertainty on collaborative management and resource recovery. The complexity of Columbia River Basin co-management includes scale, fragmentation, scientific uncertainty, and legacy. These variables lead to co-management research suggestions for the AYK Salmon Research and Restoration Program.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".