Effectiveness of northern shrimp trawls designed to reduce megafauna bycatch
Bibliographic record
Abstract
Abstract Large Greenland sharks (Somniosus microcephalus) are captured as bycatch in northern shrimp (Pandalus borealis) bottom trawls by becoming stuck in the Nordmøre grid system. Grid systems do not easily exclude Greenland sharks due to their large size (up to 6 m in length). Thus, an additional or modified bycatch reduction device (BRD) should be considered to promote a quick escape. Two experimental BRD systems were designed and tested to facilitate the escape of large-sized Greenland sharks: 1) a large escape opening at the grid (increased from 113 to 250 cm) and 2) a sieve panel with a large escape opening before the traditional grid system. Catch rates (kg tow−1) and size selectivity were compared between the experimental and traditional treatments. Results for the large escape opening treatment versus the traditional gear showed no difference in northern shrimp catch rates (P-value = 0.237), however size selectivity was different between treatments (P-value = 0.033) with a slight reduction for large length sizes. Conversely, the catch rates of northern shrimp were significantly reduced for the sieve panel treatment (18% reduction; P-value < 0.001) but showed no difference in size selectivity (P-value = 0.388) across all length classes. No Greenland sharks were observed during sea trials, and total bycatch was minimal. In conclusion, the large escape opening showed promise as a technique to reduce megafauna bycatch, such as Greenland shark, by providing a large escape area while having minimal effects on northern shrimp catch. However, the sieve panel treatment, as tested, likely loses too much northern shrimp to be considered in the fishery.
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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".