Bibliographic record
Abstract
The effect of different fishing mortality (F) and natural mortality (M), and age at first capture (t(c)) on yield-per-recruit of Atlantic croaker, Micropogonias undulatus, in the lower Chesapeake Bay and North Carolina were evaluated with the Beverton-Holt model. Independent of the level of M (0.20-0.35) or F (0.01-2.0) used in simulations, yield-per-recruit values for Chesapeake Bay were consistently higher at t((c)) = 1 and decreased continuously with increases in t(c) (2-5). Although maximum yield-per-recruit always occurred at the maximum level off (F=2.0), marginal increases in yield beyond F = 0.50-0.75 were negligible. Current F (F(CUR)) is estimated to be below the level that produces maximum potential yield-per-recruit (F(MAX)) and at or below the level of F0.1 if M ≤ 0.25. Although modeling results indicated yield-per-recruit could be maximized by reducing the current level of t(c) (t(c)=2), the resultant gains were small and did not appear to justify such management measures. Instead, it is suggested that regulatory measures be directed at maintaining the current level of t(c) in the lower Chesapeake Bay. Simulation results for North Carolina showed a pattern opposite to that shown for Chesapeake Bay, with yield-per-recruit curves increasing consistently with increases in t(c). Estimates of F(CUR) for t(c) = 1 were consistently higher than F0.1 as well as F(MAX), indicating that during the period 1979-81 Atlantic croaker were being growth-overfished in North Carolina. However, differences between Chesapeake Bay and North Carolina seem to reflect temporal rather than spatial differences in Atlantic croaker population dynamics, because data for North Carolina came from a period coinciding with the occurrence of unusually large Atlantic croaker along the east coast of the United States.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.986 | 0.986 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".