Application of Luminescent Netting in Traps to Improve the Catchability of the Snow Crab <i>Chionoecetes opilio</i>
Bibliographic record
Abstract
Abstract In this study, we investigated luminescent netting as a means to improve the catch rates of snow crabs Chionoecetes opilio. A laboratory experiment was conducted to investigate the intensity and duration of luminescence using time-lapse photography. We exposed experimental traps to five different treatments of UV light to excite the luminescent fibers in the netting. Our results showed that luminescent netting can be effectively activated to emit light, and that the resulting intensity and duration of luminescence emitted over time depends on the initial duration of UV exposure and the source of light. A fishing experiment was subsequently conducted in eastern Canada to compare the catch rate of traditional and luminescent traps, and to determine how soak time affected catch rate. Results indicate that the effect of luminescent traps on the CPUE (measured as number of crab per trap) depended on the soak time. The CPUE was significantly higher (a 55% increase) in luminescent traps that underwent relatively short soak times (~1 d), but when soak times were longer (~8 d), the CPUE was not significantly different.
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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".