The fisheries management implications of maternal-age-dependent larval survival
Bibliographic record
Abstract
A common goal of conventional fisheries management is to maintain fishing mortality at a rate that ensures an adequate level of lifetime egg production (LEP) for population sustainability. However, larvae from young spawners can experience higher mortality rates than larvae of older spawners, reducing the effect of egg production by young females (hereafter, maternal age effects). This reduction leads to an error in LEP that can be accounted for by reducing the fishing mortality rate, but raises the question of the magnitude of these errors if they are present but not accounted for. Calculations using parameters from a typical long-lived fish demonstrated that maternal age effects resulted in large errors in estimates of lifetime reproduction when there was a large contrast in the larval mortality rate extending over the reproductive life span. Errors were small when maternal age effects reduced the reproductive potential of only the very youngest spawners, at ages when a small fraction of females are mature. A specific example using the empirically derived maternal age effect for black rockfish (Sebastes melanops) indicated that errors in traditional management would be small for this species.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".