Covariation between grey seal (<i>Halichoerus grypus</i>) abundance and natural mortality of cod (<i>Gadus morhua</i>) in the southern Gulf of St. Lawrence
Bibliographic record
Abstract
More than 10 years after the collapse of Atlantic cod (Gadus morhua) fisheries in Canada, the role of increased seal populations in the decline and lack of recovery of the stocks continues to be discussed. Using removals and abundance indices from synthetic populations, we found that sequential population analysis can uncover trends in natural mortality. We used this approach to examine variation in natural mortality (M) of southern Gulf of St. Lawrence cod. M increased from the late 1970s to the mid-1990s but declined slightly recently. Results were consistent with previous work indicating that M increased in the 1980s. Changes in estimated M for this cod stock matched fluctuations in grey seal (Halichoerus grypus) abundance. The increase in grey seal abundance from the late 1970s to the mid-1990s corresponded with the increase in estimated M of cod over this time period. The correspondence between seal abundance and M of cod supports the hypothesis that seal predation may be a cause of increased M. However, the diet information available suggests that seals consume mainly juvenile cod, whereas our evidence for an increase in M is for larger cod (ages 3 years and older).
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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".