The influence of stock structure and environmental conditions on the recruitment process of Baltic cod estimated using a generalized additive model
Bibliographic record
Abstract
The recruitment process and its underlying mechanisms are among the most studied phenomena in fisheries ecology. Traditional models estimate fish recruitment assuming a direct relationship with spawning stock size. However, highly variable environmental conditions, feeding conditions, and other factors can influence and complicate the results of a simple linear regression analysis between stock and recruitment. We used generalized additive models (GAMs) to investigate the influence of environmental conditions and stock structure on the recruitment processes of Baltic cod. The interaction between abiotic factors and old spawners (>5+ years) and eggs produced by old spawners were the most significant explanatory variables. Eggs produced by young spawners have a positive impact on cod recruitment only at high levels of reproductive volume, while old spawners' eggs have the highest positive effect at low levels of reproductive volume. Here we show: (i) that the number of Baltic cod recruits is strictly dependent on the age structure of the population; (ii) that interactions between biotic and abiotic factors are crucial in explaining recruitment variability; and (iii) that GAMs are a powerful technique for defining and quantifying the intricate multidimensional relationship between biotic and abiotic variables involved in recruitment processes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".