Modelling the spatial demography of Atlantic cod (<i>Gadus morhua</i>) on the European continental shelf
Bibliographic record
Abstract
Atlantic cod (Gadus morhua) stocks across the North Atlantic have been subject to intense fishing pressure during the 20th century, and some stocks have suffered well-documented collapses. On the European shelf, cod are widely but heterogeneously distributed and are caught as part of a multispecies trawl fishery. There is a growing body of evidence that this stock is composed of substocks with potentially distinct demographic properties. As a first step towards the development of management methodologies that reflect this spatial and biological complexity, we present a spatially and physiologically explicit model describing the demography and distribution of cod on the European shelf. The computational efficiency of our implementation enables numerical parameter optimization, thus facilitating formal statistical tests of structural hypotheses. We use these methods to fit model variants embodying a variety of hypotheses about the movements of settled fish to a data set including spatial distribution information derived from International Bottom Trawl Surveys. The best-fit model emerging from this study is then used to investigate the potential effects of long-term application of a series of regional fishing closure policies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".