Trends of ocean temperature influencing snow crab catch along the Scotian Shelf
Bibliographic record
Abstract
The snow crab (Chionoecetes opilio) fishery has great economic importance for Nova Scotia, contributing $263 million to fisheries and more than one-fifth of Canadian snow crab revenues in 2021. Being a stenothermic species, snow crabs can only live within a narrow range of temperatures between −1 to 6 °C. The Scotian Shelf holds the southernmost populations of snow crab in the Atlantic and snow crab catch-per-unit-area (CPUA) in the Scotian Shelf. Trawl survey data from Ocean, because these cold-temperature requirements are a limiting factor for its distribution. This study investigates the relationship between bottom ocean temperature Fisheries and Oceans Canada from 2012 to 2021 were used. Through nonlinear modelling, CPUA (mt/km2) was regressed with respect to bottom ocean temperature along Northwest Atlantic Fisheries Organization divisions N-ENS, CFA 23, CFA 24, and CFA. The temperature-vs-CPUA relationship was unimodal for all studied years. The best-fit models explained a limited amount of variation, but CPUA consistently decreased to zero towards the highest recorded temperatures. Due to the ongoing global warming, bottom ocean temperatures across the Scotian Shelf will likely continue to increase, which thus might harm the Scotian Shelf snow crab fishery. In conclusion, this study underscores the potential impact of global warming on the economically significant snow crab fishery in the Scotian Shelf. The findings serve as a critical alert to the possible consequences of rising ocean temperatures, thereby contributing to our understanding and preparation for the future of marine ecosystems and industries.
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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 | 0.000 | 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".