Artificial lights improve the catchability of snow crab ( Chionoecetes opilio ) traps
Bibliographic record
Abstract
This study investigated the behaviour and commercial catchability of snow crab (Chionoecetes opilio) in response to different low-powered LED lights under laboratory and field conditions. We created a novel choice-experiment in a laboratory setting in which we investigated the behaviour of snow crab in response to coloured LED lights. The results showed that snow crab movement was dependent on light colour, with animals choosing to move toward blue and white lights, away from purple lights, and no detectable effect for green and red lights. We then conducted two field experiments to investigate the effect of the same LED lights on the catch rates of commercial traps during the 2016 snow crab fishery on the east coast of Newfoundland and Labrador. Results from the first field experiment showed that adding white and purple LED lights into baited traps significantly improved Catch Per Unit Effort (CPUE) by 77% and 47% respectively. Results from the second field experiment showed that unbaited traps equipped with only LED lights (no bait), could also catch snow crab in comparable amounts to traditional baited traps, with soak time and depth explaining some of the variation in CPUE. Taken together, these experiments suggest that fishing enterprises can improve their catching performance and profitability by adding LED lights to their traps, or by using LED lights as a bait replacement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".