Shorter trawls improve size selection of northern shrimp
Bibliographic record
Abstract
Discards of small northern shrimp (Pandalus borealis) are a problem in the Skagerrak northern shrimp trawl fishery. To reduce catches of small shrimp, we studied the effect of trawl belly length on size selectivity in November 2017 and June 2018 onboard 15 and 27 m double-rigged shrimp trawlers. The selectivity of the vessels’ standard trawl was compared with a trawl differing only in the belly length, being 37% shorter. The trawls had 40 mm bottom panels and cod ends of 35 mm mesh sizes. Eleven and 14 hauls were made, respectively, in 2017 aboard the 15 m vessel and in 2018 aboard the 27 m vessel. The trawls fished shrimp above 19 mm carapace length equally, while catch rates of shrimp below 15.5–16 mm carapace length in the shorter trawl were more than halved. The results were consistent between the two vessels. In short, modifying trawl length is a simple design modification that can reduce catches of small shrimp. Bycatch of Norway pout (Trisopterus esmarkii) was slightly reduced in the shorter trawl, unrelated to fish length.
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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) | 0.000 | 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) | 0.003 | 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".