Bycatch reduction in the deep-water shrimp (<i>Pandalus borealis</i>) trawl fishery by increasing codend mesh openness
Bibliographic record
Abstract
In most trawl fisheries, drag forces tend to close the meshes in large areas of diamond mesh codends, negatively affecting their selective potential. In the Barents Sea deep-water shrimp (Pandalus borealis) trawl fishery, selectivity is based on a sorting grid followed by a diamond mesh codend. However, the retention of juvenile fish as well as undersized shrimp is still a problem. In this study, we estimated the effect of applying different codend modifications, each aimed at affecting codend mesh openness and thereby selectivity. Changing from a 4-panel to a 2-panel construction of the codend did not affect size selectivity. Shortening the lastridge ropes of a 4-panel codend by 20% resulted in minor reductions for juvenile fish bycatch, but a 45% reduction of undersized shrimp was observed. Target-size catches of shrimp were nearly unaffected. When the codend mesh circumference was reduced while simultaneously shortening the lastridge ropes, the effect on catch efficiency for shrimp or juvenile fish bycatch was marginal compared to a 4-panel codend design with shortened lastridge ropes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".