A novel method for modeling age and length selectivity of sockeye salmon as applied to the Bristol Bay Port Moller test fishery
Bibliographic record
Abstract
This paper presents a novel approach for assessing sources selectivity in test fisheries using the Port Moller test fishery (PMTF) as a case study. The PMTF intercepts sockeye salmon (Oncorhynchus nerka) migrating to Bristol Bay, Alaska, to estimate run strength and timing. In 2011, the mesh size of gillnets used in the test fishery was decreased for half of the net panels to generate more accurate run estimates by correcting for greater selectivity of larger 3-ocean fish (fish that have spent 3 years in the ocean) relative to smaller 2-ocean fish (fish that have spent 2 years in the ocean). Here, we quantify two sources of age selectivity in the PMTF program, length selectivity parameterized by mean fish length (which should be corrected by the net mesh change) and length-independent selectivity, which we refer to as residual program selectivity (which would not be impacted by the net mesh change), both before and after the net change. Model parameters of selectivity show strong support length selectivity was eliminated, but residual program selectivity still existed after the reduction in net mesh size. Our results demonstrate the necessity of considering both vulnerability and accessibility to fishing gear when assessing selectivity in test fisheries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".