Risk factors associated with soft-shelled lobsters (<i>Homarus americanus</i>) in southwestern Nova Scotia, Canada
Bibliographic record
Abstract
Soft-shelled lobsters pose economic challenges to the lobster industry due to low meat yields and survivability during holding and transportation. Our objectives were to describe spatio-temporal patterns of soft-shelled lobsters in southwestern Nova Scotia, and identify environmental and lobster-related factors associated with shell quality. We analyzed data obtained from a broad-scale, intensive monitoring project and remotely sensed water temperatures. Mixed-effect logistic regression and linear regression methods analyzed more than 130 000 samples collected between 2004 and 2014. The annual overall prevalence of soft-shelled lobsters ranged from 9% to 38% and varied significantly among fishing areas. Shell quality was influenced by sex and size, and in the 2 months before the fishing season, lower water temperatures (4–6 weeks prior to sampling) were associated with reduced prevalence of soft-shells. High annual variability of soft-shell prevalence, that water temperature alone could not explain, suggests that adjusting fishing seasons, arbitrarily, in two fishing areas will not improve the overall shell quality of landed lobsters. Further research is needed to evaluate the effects of long-term temperature and ecosystem changes on lobster health in eastern Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".