Assessing the potential for stock enhancement in the case of the Chesapeake Bay blue crab (<i>Callinectes sapidus</i>)
Bibliographic record
Abstract
In certain cases of severely depleted fishery stocks, combining stock enhancement with traditional management techniques may be a useful way of returning stocks to an exploitable size. The Chesapeake Bay stock of blue crabs (Callinectes sapidus) has declined over the past decade and appears to be recruitment-limited, making it an appropriate candidate for enhancement efforts. This study serves as a first step in determining whether large-scale enhancement of blue crab stocks is feasible. Four hatchery-raised cohorts of 4000 10 000 (25 000 in total) juvenile (630 mm carapace width, 5870 days old) crabs were released in upper Chesapeake Bay coves. Sixty days after release, these crabs constituted 22%79% of all crabs in the hatchery-crab size range (corresponding to an enhancement level of 28%366%). Crabs released earlier in the summer reached maturity at the age of 6 months, younger than their wild counterparts. Estimated survivorship to maturity was 16%20% for early-released crabs and 515% for late-released crabs. Late-released crabs, like wild crabs, had to overwinter before becoming mature. Our study suggests ways to improve success of hatchery-raised individuals that can be broadly applied across taxa. The results also contribute specifically to determining whether large-scale stock enhancement is possible in the case of the Chesapeake Bay blue crab.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".