An individual-based modeling approach to spawning-potential per-recruit models: an application to blue crab (<i>Callinectes sapidus</i>) in Chesapeake Bay
Bibliographic record
Abstract
An individual-based modeling approach to estimate biological reference points for blue crabs (Callinectes sapidus) in Chesapeake Bay offered several advantages over conventional models: (i) known individual variation in size and growth rate could be incorporated, (ii) the underlying discontinuous growth pattern could be simulated, and (iii) the complexity of the fishery, where vulnerability is based on size, shell status (e.g., soft, hard), maturity, and sex could be accommodated. Across a range of natural mortality (M) scenarios (0.3751.2·year1), we determined the exploitation fraction (µ) and fishing mortality (F) that protected 20% of the spawning potential of an unfished population, the current target. As M increased, µ20% and F20% decreased. Assuming that M = 0.9·year1, our models estimated µ20% = 0.45, which is greater than field-based estimates of µ in 64% of the years since 1990. Hence, the commercial fishery has likely contributed to the recent population decline in Chesapeake Bay. Comparisons of our results with conventional per-recruit approaches indicated that incorporating the complexity of the fishery was the most important advantage in our individual-based modeling approach.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".