Alternative bait trials in the Barents Sea snow crab fishery
Bibliographic record
Abstract
Commercial harvesting of snow crab (Chionoecetes opilio) in the Barents Sea started in 2012 by Norwegian fishing vessels. This new fishery has significant bait requirements, representing an emerging conservation challenge. In this study, we evaluate the performance of five alternative (natural) baits manufactured from the waste stream of existing and sustainably managed harp seal (Pagophilus groenlandicus) and minke whale (Balaenoptera acutorostrata) capture. Five different types of new bait were evaluated, including seal fat (SF), seal fat with skin (SFS), seal meat with bone (SMB), whale fat with skin (WFS), and whale meat with fat (WMF). A comparative fishing experiment was conducted onboard a commercial snow crab fishing vessel in the Barents Sea (May–June, 2016) to evaluate the performance of traditional bait (squid, Illexs spp.) and alternative baits at catching snow crabs. Performance of the different baits were compared on the basis of the number of commercial crab caught per trap haul catch per unit effort (CPUE) and carapace width (CW). Our results showed that SF and SFS performed equally well to traditional bait, with no statistical difference in CPUE (p-value = 0.325 and 0.069, respectively). All of the other experimental baits significantly decreased CPUE, when compared to squid. No significant effect of bait treatment on CW was detected and the cumulative distribution of CW was the same between control traps and each of the bait treatments. Overall the results indicated that SF and SFS represent a viable alternative to replace traditional bait, addressing a key conservation challenge in this bait intensive 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".