Location, orientation, and economic performance of low-powered LED lights inside snow crab traps in eastern Canada
Bibliographic record
Abstract
This study investigated the effect of installing underwater Light-Emitting Diode (LED) lights in different locations and orientations inside baited traps targeting snow crab Chionoecetes opilio off the coast of Newfoundland and Labrador, Canada, as well as the economic performance associated with using lights in this fishery. Our results showed no significant differences in catch per unit effort (CPUE) for both legal and sublegal-sized crab among the different experimental treatments, however all of the experimental (illuminated) traps harvested significantly more crab (+53% on average) than control traps (without lights). Longer soak time did not affect the catch rate of the control traps, however it significantly increased the catch rate for the illuminated traps. The proportion of legal-sized and sublegal-sized crab accounted for 73% and 27%, respectively for both control and illuminated traps. In addition, there were no significant differences in crab size distributions between pairwise comparisons. In terms of economic feasibility, we show that an investment in LED lights by a fishing enterprise will require additional variable costs, however our analysis reveals the financial break-event point can be reached after approximately two years. A profit of $164,920 CDN per vessel was predicted during the life cycle of a typical light (e.g. 14 years), compared to traditional capture methods (without lights). This gain was proportional with crab prices and allocated quota level. These results suggest that fishing enterprises can increase their profitability by using LED lights in the snow crab 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".