Effect of water temperature on catchability of Atlantic cod (Gadus morhua) to the bottom-trawl survey in the southern Gulf of St Lawrence
Bibliographic record
Abstract
Correlations between catch rates in bottom-trawl surveys and indices of environmental conditions or fish distribution have been attributed to effects of the environmental conditions or fish distribution on catchability to the survey. We tested this hypothesis using data on cod in the southern Gulf of St Lawrence. Survey catch rates of cod were significantly correlated with indices of bottom temperature and of cod temperature and depth distributions. Correlations were in the directions expected on the basis of predicted effects on catchability or availability to the survey. However, tests involving calibrations of sequential population analysis (SPA) or residuals from multiplicative analyses of the survey catch rates (with terms for yearclass, age and cumulative mortality) did not support the hypothesis that these correlations resulted from effects on catchability to the survey. These tests provided no support for an effect of cod temperature or depth distribution on catchability. They provided some support for an effect of bottom temperature conditions on availability to the survey, but this effect on availability was not reflected by the correlations observed between bottom temperature indices and survey catch rates. We conclude that adjustments for effects on catchability should be based on relationships with inconsistencies in survey catch rates instead of relationships with the catch rates themselves. These adjustments could be incorporated in calibration of the SPA or calculated based on relationships with residuals from models relating survey catch rate to yearclass, age and fishing mortality. However, such adjustments were negligible in the case of southern Gulf of St Lawrence cod.
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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) | 0.000 | 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".