Increased catches of snow crab (Chionoecetes opilio) with luminescent-netting pots at long soak times
Bibliographic record
Abstract
Luminescent netting increases the catch rate of snow crabs ( Chionoecetes opilio ) over short soak times (1 d), however the commercial fishery often requires longer soak periods, up to1 week. Building on previous research, this study investigated the catch efficiency and size selectivity of pots with luminescent netting over long soak times (144–336 h) in the inshore snow crab fishery of Newfoundland, Canada. A total allowable catch and individual quota allocation management system for snow crab is regulated in Canada and using luminescent netting to increase catch rates would reduce the carbon footprint of the fishery by reducing days fished. Our results showed that luminescent pots had a 21.6 % and 18.3 % higher catch-per-unit-effort (CPUE; number of crabs per pot) of legal-sized crab and sub-legal sized crab, respectively, than control pots; with no difference for soft-shelled crab. Additionally, no significant differences were shown for size selectivity over the range of carapace widths observed between luminescent and control pots. Little other bycatch (female snow crab and unwanted species) were caught in either pot treatments. This study shows that luminescent netting increases the efficiency of the snow crab fishery, which provides economic and environmental benefits.
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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".