Effect of light-emitting diodes (LEDs) on snow crab catch rates in the Barents Sea pot fishery
Bibliographic record
Abstract
Abstract Snow crab (Chionoecetes opilio) has become an important species for the Norwegian seafood industry since its first commercial harvest in 2012. However, periodically catch rates can be low, causing a financial strain on the fishery. Thus, improving the catch rate of existing pot designs has the potential to significantly improve the profitability of fishing enterprises. In this study, we investigated whether the addition of low-powered purple and white light-emitting diode (LED) fishing lights inside the pots could improve catch rates of snow crab in the Barents Sea. Results showed that pots with purple lights harvested a 12.8% higher catch per unit effort (CPUE; number of crab per pot) of legal-sized crab, which was significantly more than the control pots (p = 0.035); pots with white lights did not catch significantly more crab (p > 0.05). Pots equipped with only light (no bait) caught very few crabs and were not considered a viable alternative. Although purple LEDs increased snow crab capture, the economic benefits of using underwater lights in pots remains unclear given the high capital investment required.
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) | 0.002 | 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".