Artificial light improves size selection for northern shrimp (<i>Pandalus borealis</i>) in trawls
Bibliographic record
Abstract
Size selection in the northern shrimp (Pandalus borealis) trawl fisheries is a widely studied topic. While the focus has largely been on codend and grid selectivity, studies have shown the importance of other design changes and the application of artificial light to evoke behavioural responses. LED lights of three different colours — green (∼470–580 nm), white (∼425–750 nm) and red (∼580–670 nm) — were mounted in the belly section of a shrimp trawl to investigate their influence on the overall selectivity of the trawl. The study was conducted using a twin-trawl setup, one with light and the other without light. For catch-comparison analysis, a polynomial regression with random effects was applied. The number of valid hauls with green, white and red lights were eleven, eight, and nine, respectively. All lights tested significantly affected the length-dependent retention of shrimp. Green light had the greatest effect, red the least. Significant loss was observed for shrimp below 17.5 mm carapace length (CL) for green light, 19.5 mm CL for white and 20.8 mm CL for red light.
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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".