Effect of a quality-improving cod end on size selectivity and catch patterns of cod in bottom trawl fishery
Bibliographic record
Abstract
A new cod-end concept developed and tested exhibited significantly improved quality of caught cod (Gadus morhua) compared with that of the conventional cod end used in the Barents Sea bottom trawl fishery. However, the design of the new quality-improving cod end raised concerns about its size selectivity and the possibility that higher retention probability could negatively impact the catch pattern by increasing the proportion of undersized cod. Therefore, the goal of this study was to quantify and compare the size selectivity and catch pattern for cod when deploying, respectively, the conventional and new quality-improving cod end in the Barents Sea bottom trawl fishery. The new quality-improving cod end had significantly lower relative size selectivity than the conventional cod end, but no significant difference in the catch patterns was detected in the trawl. Further, estimation of the total size selectivity in the trawl revealed that the increased retention of small cod when using the quality-improving cod end was minor. Hence, despite the reduced selectivity, the quality-improving cod end can be used with low risk of retaining small cod.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".