Enhancing bycatch escape in trawl fisheries through flow manipulation: a study on Gadoids
Bibliographic record
Abstract
Flow manipulation offers a promising yet under-utilized method for enhancing bycatch escape in demersal trawl fisheries by exploiting fish swimming behaviors, such as flow refuging. We investigated to what extent different Gadoids use a low-flow zone to escape from a trawl. A 360° radial escape opportunity was created by placing a gap in the codend within the low-flow zone generated by a tarpaulin funnel. An additional tarpaulin deflector expanded the low-flow zone to prevent fish from holding around the funnel. This proof-of-concept design was developed using flume tank trials and computational fluid dynamics, and tested at sea. Substantial escape rates were observed for cod ( Gadus morhua), haddock ( Melanogrammus aeglefinus), and whiting ( Merlangius merlangus). Cod showed an increased escape rate with length, while no such trend was seen in haddock or whiting. We tested two gap sizes and found that a shorter gap size resulted in higher escape rates for undersized haddock and whiting, while cod escape was unaffected by gap size. These findings suggest species-specific interactions between hydrodynamics and behavior, useful for bycatch reduction in trawl 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 | 0.000 | 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) | 0.000 | 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".