In-season harvest and effort estimates for the 2020 Kuskokwim River subsistence salmon fisheries during block openers
Bibliographic record
Abstract
ABSTRACT: Management of the Kuskokwim River Chinook Salmon (Oncorhynchus tshawytscha) subsistence fishery has historically been conducted with minimal in-season harvest information. Because of this lack of information, it is challenging to make in-season management decisions regarding fishing opportunities to simultaneously achieve conservation and subsistence harvest objectives, particularly during years of weak Chinook Salmon runs. In response to an uncertain 2020 Kuskokwim River Chinook Salmon run, and given recent years with low returns, the United States Fish and Wildlife Service in collaboration with the Bering Sea Fishermen’s Association and the Orutsararmiut Native Council, collected data to produce in-season subsistence salmon harvest estimates from that portion of the mainstem Kuskokwim River within the boundaries of the Yukon Delta National Wildlife Refuge between and including the villages of Tuntutuliak and Akiak. Using methods developed and refined during 2016 – 2018, The author estimated the total subsistence salmon harvest in this area was 35,500 (95% CL: 29,310 – 42,470) during seven fishing opportunities between June 3 and June 24, 2020. Most salmon harvested were Chinook Salmon (23,210; 95% CL: 19,060 – 28,050), followed by Sockeye Salmon (O. nerka; 6,710; 5,170 – 8,380), and Chum Salmon (O. keta; 5,590; 4,120 – 7,350). Methodologies refined during this study will be useful to structure future efforts to estimate subsistence salmon harvests on the Kuskokwim River as well as other fisheries with similar characteristics.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".