Optimum sampling levels in discard sampling programs
Bibliographic record
Abstract
This paper presents optimum sampling levels in discard sampling programs considering cost and precision objectives simultaneously and explores their dependence on both variables. The analysis is based on the Irish discard program: an onboard-observer voluntary sampling scheme aimed at estimating discard rates in trawl fisheries. Multistage analysis was performed to establish the precision levels achieved in the past, and a cost function was determined to estimate the financial cost of the program. Gear, fishing ground, targeted species, and International Council for the Exploration of the Sea (ICES) divisions were the main factors affecting discarding, together with random effects of the three nested groups considered: haul, trip, and vessel. Reductions in the present budget will imply only marginal decreases in precision, although changes in cost variables can have an impact on sampling levels. On the other hand, increasing the target precision by one-half will imply a considerable increase in sampling and associated cost, which will be difficult if not impossible to achieve. Finally, the analysis by fleet components suggests a marked increase in sampling levels, which emphasizes the importance of clearly stated discard sampling objectives.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".