Improving escape efficiency in selective devices with the use of a dark tunnel
Bibliographic record
Abstract
Despite management efforts, like the implementation of escape panels and openings, unwanted catch remains a challenge in demersal trawl fisheries. Studies report that selectivity in escape panels and openings can be low. We explore the selective potential of a large escape opening placed at the aft of the trawl. We then examine if adding a dark tunnel behind the escape opening can increase the escape efficiency of fish by triggering a station-holding behaviour. Our results showed limited escapement through the large escape opening; however, significant for narrow length ranges of some species. Adding the dark tunnel significantly increased the escapement for all analysed species, with escapement up to 70% (40%–83%) and 63% (8%–93%) for roundfish and flatfish, respectively. As target species, a loss of crustaceans up to 85% (60%–96%) highlighted the importance of optimising the integration of the dark tunnel in demersal trawls. Providing the dark tunnel is integrated correctly, our results suggest that currently implemented escape panels and openings with low selective efficiency could be substantially improved by simple means like the dark tunnel.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".