Developing management plans for sprat (<i>Sprattus sprattus</i>) in the Celtic Sea to advance the ecosystem approach to fisheries
Bibliographic record
Abstract
Sprat are commercially valuable and are an important component of the North-East Atlantic ecosystem as major predators of zooplankton, competitors with herring, and prey for piscivorous fish, marine mammals, and seabirds. Despite this, insufficient information exists for Celtic Seas sprat, one of five North-East Atlantic stocks, to estimate stock status. To ensure the sustainable exploitation of sprat, the health of the Celtic Seas ecosystem, and the wider fisheries sector, we conduct a management strategy evaluation to stress test the current single-species advice framework. The aim is to evaluate whether ecosystem objectives can be achieved under single-species maximum sustainable yield and precautionary advice frameworks. An operating model was conditioned on life history theory and strategic information from ecosystem models. We showed that in-year advice using an empirical rule could achieve management objectives and help balance the trade-offs between fishing activities and ecosystem health. The approach allows ecosystem understanding to be incorporated within existing precautionary and maximum sustainable yield frameworks to provide a robust management framework that can meet multiple objectives despite uncertainty.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".