A Test of Deriving Sex-Composition Data for the Directed Pacific Halibut Fishery via At-Sea Marking
Bibliographic record
Abstract
Abstract Sensitivity analyses have identified uncertainty regarding sex ratios within commercial landings of Pacific Halibut Hippoglossus stenolepis as an influential source of variance within annual stock assessments for this species in U.S. and Canadian waters. Sex composition of dockside landings cannot be directly observed because all retained fish must be eviscerated at sea, and sex cannot be visually determined in the absence of the gonads. In the current study, a marking program was evaluated in which sex-specific marks were applied by fishers to their retained catch, the mark was recorded during dockside monitoring, and the accuracy of the recorded sexes was validated using genetic techniques. The chosen marks (two vertical cuts in the dorsal fin for females and a single cut in the operculum of males) were considered by fishers to be easy to apply during at-sea processing and produced sex-ratio estimates that were equivalent to genetic results for 65% of sampled landings. However, vessel- and region-specific accuracy was variable. Additional incentives to encourage vessels to participate in the program, continued outreach, or potentially a regulatory requirement to mark fish would be required to produce sufficient data to satisfy stock assessment needs, and ongoing validation would likely need to accompany such a program to ensure consistent and acceptable data quality.
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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 | 0.056 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".