Atlantic cod (Gadus morhua) distribution response to environmental variability in the northern Gulf of St. Lawrence
Bibliographic record
Abstract
Atlantic cod ( Gadus morhua ) distribution patterns and the behavioral (site fidelity), biotic (prey and predators), and environmental factors that determine them are fundamental to cod’s historic importance as a commercial species in the North Atlantic. Using classification and regression tree analysis (CART), we compared two periods (1991–1995 and 1998–2004) with contrasting bottom temperature and salinity regimes to determine regional factors that best explained cod distribution and catch weight per tow from summer surveys in the northern Gulf of St. Lawrence (the feeding period of cod). The classification tree analysis indicated that the presence or absence of cod was chiefly determined by depth in both of these periods. In contrast, the regression tree analysis determined that cod catch weight distributions were explained by different variables in each period. In the colder period (1991–1995), the distribution of catch weights was explained well by environmental variables (bottom temperature, salinity, depth); however, in the warmer period (1998–2004), distributions were best explained by variables from the previous year. These results indicate that the spatiotemporal dynamics of environmental conditions are likely to influence the loyalty of cod to specific feeding grounds and imply that cod responses to the environment could be susceptible to long-term environmental (e.g., bottom–habitat) and climate change.
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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) | 0.000 | 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 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".