In-season monitoring of harvest and effort from a large-scale subsistence salmon fishery in western Alaska
Bibliographic record
Abstract
In-season management of salmon harvest requires real-time data. Specifically, following a brief period of open fishing, knowledge of harvest outcomes is useful when deciding the nature of subsequent fishing periods. This in-season management strategy is relatively new to the lower Kuskokwim River of western Alaska, where depressed salmon runs have caused restrictions to the subsistence fishery. We have developed an in-season monitoring program to rapidly inform managers about fishery outcomes from short-duration (6–24 h) fishery openings. Completed trip information and one or more aerial surveys are combined to estimate daily effort and harvest from drift gillnet fishers spanning 11 communities and ~130 river kilometers. We present a re-analysis of the 40 monitored openers in June–July 2016–2023, and validate harvest estimates of Chinook, chum, and sockeye salmon by comparing them to post-season estimates derived from an independent long-term monitoring program. Our results indicate that the program has produced estimates of sufficient quality to inform in-season managers, although it will likely need alterations to be successful in years with less restricted fishing.
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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 | 0.000 |
| 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".