Long-term trends in juvenile American lobster populations across nine lobster fishing areas in Nova Scotia, Canada
Bibliographic record
Abstract
Analyzing juvenile American lobster populations using fishery-independent data enhances understanding of population dynamics and supports fishery sustainability. This repeated cross-sectional study investigated trends in juvenile lobster populations by analyzing size distributions from nine Lobster Fishing Areas (LFAs) in Nova Scotia, Canada. Data were collected in season from lobsters sampled using ventless research traps between 2003 and 2023. Mixed effect linear and logistic regression models assessed spatial and temporal influences on mean carapace length (CL) and proportions of juvenile lobster. This study provides a 20-year overview of mean CL and juvenile probability, accounting for factors including water depth, sampling month, and lobster sex. Results showed significant variation in temporal effects on mean CL and proportion of juvenile lobster between LFAs. Decreasing trends in sampling juvenile lobsters were observed along the Northeastern coast of Cape Breton and in a highly productive area on the South Shore of Nova Scotia. In contrast, other LFAs showed more resilience, with the southwestern area displaying a relatively stable trend. The findings highlight variability in juvenile lobster trends across LFAs, which reflect the relative effectiveness of local management measures and provide insights to inform management decisions in Nova Scotia.
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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 | 0.001 |
| 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".