Chocolate squid (Todarodes pacificus) bait reduces snow crab catch rates
Bibliographic record
Abstract
This study evaluated the performance of chocolate squid (Todarodes pacificus) bait as a lower-cost alternative to traditional squid bait in a snow crab (Chionoecetes opilio) fishery in Eastern Canada. The results showed that pots with chocolate squid bait had significantly lower catch rates compared to pots with traditional squid bait. The chocolate squid bait significantly reduced the number of crabs caught per pot by 13.99% and the weight of crabs caught per pot by 2.88 kg. Although the chocolate squid bait is cheaper, the decrease in catch rates could require increased effort to reach individual quotas, which could lead to longer or more frequent fishing trips and more traps being fished, increasing fuel consumption and habitat disruption, resulting in negative environmental impacts. Finding alternative lower-cost baits that maintain catch rates remains a key priority for reducing operational costs and minimizing environmental impacts in the snow crab fishery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".