Pacific Salmon: Ecology and Management of Western Alaska’s Populations
Bibliographic record
Abstract
Abstract.—Jurisdictional boundaries add a layer of complexity to the already difficult task of managing fisheries. This paper outlines the challenges of cross-border management in the Great Lakes of North America and the Arctic-Yukon-Kuskokwim (AYK) region of Alaska and Yukon Territory and discusses the role of governance regimes established to facilitate fishery management in those regions. Management of the multi-jurisdictional Great Lakes fishery occurs without direct federal oversight. Eight Great Lakes states, the province of Ontario, and several U.S. tribes manage the sport, commercial, and subsistence fisheries within their jurisdiction, though the Canadian and U.S. federal governments make important contributions as well. To help in the development of shared fishery policies, the nonfederal jurisdictions, with the support of the federal agencies and the binational Great Lakes Fishery Commission, signed A Joint Strategic Plan for Management of Great Lakes Fisheries, a voluntary, consensus-based agreement. Similar to the Great Lakes, political diffusion is also a characteristic of management of salmon in the AYK region. AYK fishery management must consider state, federal, provincial, territorial, and international treaty jurisdictions. Different from the Great Lakes, federal involvement is much greater in the AYK region because of abundant federal lands combined with federal legislation (e.g., Alaska National Interest Lands Conservation Act of 1980) and the presence of international waters and treaties. Based on lessons from the Great Lakes, a pathway to increasing cooperation and effectiveness of AYK salmon management includes: identification of common interests; adoption of shared goals; information sharing; building of relationships among agencies and individuals; and use of consensus decision-making and accountability mechanisms. Connecting all of the agencies affecting the salmon life cycle and fisheries in the AYK region through an appropriate forum or institution would enhance cooperative and effective AYK salmon management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".