Evaluation of harvest control rules for a group of interacting commercial stocks using a multispecies MSE framework
Bibliographic record
Abstract
In this study, a multispecies gadget model (GadCap) simulating the interactions among the Flemish Cap cod (Gadus morhua), redfish (Sebastes spp.), and shrimp (Pandalus borealis) has been incorporated as the operating model in a management strategy evaluation (MSE) framework (a4a-FLR) to test the performance of multiple combinations of harvest control rules (HCRs) for the three stocks when recruitment uncertainty and assessment error are accounted for. The results indicate that due to the strong trophic interactions, it is not possible to achieve the precautionary exploitation of all the stocks at the same time. Maintaining shrimp biomass above the limit reference point (Blim) would require unsustainable fishing pressure on cod and redfish to reduce predation mortality. In contrast, maintaining cod biomass above Blim would involve high predation on and high risk of collapse of the shrimp and redfish stocks. The implementation of alternative two-stage HCRs would reduce predation, resulting in higher productivity and lower probability of collapse for cod and redfish. The results of this study support the need of accounting for species interactions when designing management strategies for a group of interdependent commercial stocks.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".