Oysters beget shell and vice versa: generating management goals for live oysters and the associated reef to promote maximum sustainable yield of <i>Crassostrea virginica</i>
Bibliographic record
Abstract
Populations of the eastern oyster (Crassostrea virginica) have experienced declines from overfishing and disease throughout much of its US range, though development of maximum sustainable yield (MSY) management criteria has been elusive. This is due in part to the discordance between oyster spawning stock and recruits, as the classic stock–recruitment model does not account for the requirement of shell substrate on which recruits settle. This issue was recently addressed with the development of a surface area–recruitment model, which is herein incorporated into a simulation analysis to estimate MSY-based reference points for C. virginica in the Delaware Bay. Simulations demonstrate that at low natural mortality, fishing mortality (F) may be sustainable at values between 10% and 15%; however, if disease or other mortality-enhancing processes occur, the margin of error in fishing is small and may quickly lead to population and reef collapse, emphasizing a precautionary F < 10%. The MSY-based reference points generated here provide rebuilding goals for the oyster fishery and reef management on fished and unfished reefs and the framework from which shell-planting can be incorporated and optimized in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".