Lobster Yield Dynamics in a Warming Ocean: A Generalized Linear Modeling Case Study in Prince Edward Island, Canada
Bibliographic record
Abstract
The lobster fishery is the third largest industry in Prince Edward Island (PEI), Atlantic Canada. Rising water temperatures due to global warming are impacting the successful completion of the lobster life cycle, which is heavily dependent on water temperature. This study investigated the relationship between lobster landings and sea surface temperature (SST) in PEI. Using Generalized Linear Models (GLM), we identified a significant correlation between annual historical lobster landings and monthly sea surface temperatures (SST) in the waters around PEI from 1990 to 2021. Considering the 5-8 year maturation period of lobsters, we applied a lagged SST structure over an 8-year period and used a Generalized Linear Model (GLM) to evaluate the relationship between historical SST and lobster landings. Our findings suggest that historical increases in SST are correlated with changes in lobster landings. Given the known sensitivities of lobster life cycles (i.e., spawning, larval development) and behavior (i.e., mating) to high ambient water temperature, our study also offers important insights for future fishery management under anticipated climate change scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".