Life-history evolution and elevated natural mortality in a population of Atlantic cod (<i>Gadus morhua</i>)
Bibliographic record
Abstract
Fisheries-induced evolution has been hypothesized to delay the recovery of collapsed fish stocks through effects on their productivity. The cod stock in the southern Gulf of St. Lawrence (SGSL) collapsed in the early 1990s and has shown no recovery since then, due mainly to high natural mortality of adult cod. Age and size at maturation of SGSL cod decreased sharply over time in cohorts produced in the 1950s and 1960s, likely reflecting an evolutionary response to intensified fishing, and have remained low since then, despite severe reductions in fishing mortality over the past 15 years. A predicted consequence of early maturation is increased natural mortality due to higher costs to reproduction. Early maturation may be a cause of increases in natural mortality of SGSL cod in the 1970s but does not appear to be related to the much larger increases since then. Instead, the current high natural mortality of SGSL cod appears to be primarily a cause, rather than a consequence, of the continued early maturation in this population, now replacing fishing mortality as the agent of selection favouring early maturity. This striking example of the failure to reverse fisheries-induced evolution by relaxing fishing pressure emphasizes the need for management strategies that minimize the chances of harvest-induced genetic 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) | 0.001 | 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".