Periodic fluctuations in recruitment success of Atlantic cod
Bibliographic record
Abstract
Autocorrelation in recruitment success of fish is frequently reported, but the underlying mechanisms are generally only vaguely alluded to. We analysed recruitment success of 21 cod (Gadus morhua) stocks in the North Atlantic to investigate possible common causes of autocorrelation in recruitment. We found autocorrelation and periodic fluctuations in recruitment success and adult growth in just above half of the stocks considered and investigated six possible underlying mechanisms. With three exceptions, the variations in recruitment success were not significantly related to temperature or growth anomalies, indicating that the variation was not caused by temperature-dependent survival or growth-dependent spawning products. Further, a link between recruitment and subsequent spawning biomass could not explain the observed recruitment patterns. Slow-growing cod stocks tended to exhibit longer cycles and positive autocorrelations consistent with dilution of predation mortality by adjacent large year classes or age reading errors, whereas fast-growing cod stocks showed shorter cycles and no significant autocorrelation at lag 1. Both types exhibited significant negative autocorrelations consistent with cannibalism at one or more lags greater than lag 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".