Use of escape gaps in Barents Sea snow crab (Chionoecetes opilio) fishery: Can it reduce bycatch of undersized crabs?
Bibliographic record
Abstract
Snow crab ( Chionoecetes opilio ), like several other crustacean species, are commonly captured using trap gear, which is designed as conical pots. Many such pot fisheries employ some selectivity mechanisms that allow release of captured small or undersized individuals on the seabed, thus improving survival of escapees and reducing workload for the fishers. In snow crab pot fisheries, the selectivity mechanism is based mainly on crab escape through netting meshes (diamond-shaped mesh with sizes ranging from 120 – 140 mm). However, several observations have shown that in commercial snow crab pot fisheries, catches contain undersized snow crabs. Therefore, this study aimed to test the use of escape gaps in the Barents Sea snow crab fishery to evaluate whether it can reduce the bycatch of undersized crabs and sharpen size selectivity. The results showed that both standard pots using mesh selection and test pots with escape gaps reduced catches of undersized snow crabs. However, pots with escape gaps significantly reduced the capture of undersized crabs compared to pots that used only netting mesh selection. This result can be important for the commercial fishery, especially considering areas with larger abundances of small snow crabs, where improved size selection could result in reduced workload for catch sorting and potential crab mortality due to the associated handling.
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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".