Dynamic of the Flemish Cap commercial stocks: use of a Gadget multispecies model to determine the relevance and synergies among predation, recruitment, and fishing
Bibliographic record
Abstract
Multispecies modeling is being increasingly accepted in stock assessment, especially in the context of an ecosystem approach to fisheries management (EAF). To achieve a future implementation of an EAF in the Flemish Cap, we present a multispecies model developed in Gadget, which covers the main commercial stocks over the period 1988–2012: cod (Gadus morhua), redfish (Sebastes spp.), and northern shrimp (Pandalus borealis). The model highlights the interdependent dynamic of these stocks and reveals strong interactions among recruitment, fishing, and predation (including cannibalism). These drivers have shown marked changes in their relative importance by species, age, and length over time, producing a transition from a traditional redfish- and cod-dominated system in the early 1990s to an intermediate shrimp and other fish species state by the late 1990s and in turn back to something close to the initial state by the late 2000s. The multispecies model developed in this paper shows that disregarding the species interactions would lead to serious underestimates of natural mortality and overestimations of the exploitable biomass and highlights the need to move beyond single-species management in this highly coupled ecosystem.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".