Evaluation of harvest strategies for pelagic sharks taking ecological characteristics into consideration: an example for North Pacific blue shark
Bibliographic record
Abstract
We have developed a population dynamics model that considers the spatial segregation by sex and ontogenetic stages of pelagic sharks. The model was used to evaluate the performance of harvest strategies based on ecological characteristics. We proposed five harvest strategies for longline fisheries based on the ecological characteristics of blue shark (Prionace glauca) in the North Pacific. Management objectives for depleted populations are to increase yield to the level that provides maximum sustainable yield through increases in biomass without collapsing the fishery. Deterministic and stochastic analyses were undertaken. We determined that the harvest of male sharks was robust to uncertainty of environmental changes, reducing the likelihood of fishery collapse and stabilizing yield and mean biomass. The harvest of male sharks was also robust to the uncertainties of biological parameters such as natural mortality and steepness. These results suggested that if there was no sperm limitation or impact on the mating behavior of the species, the harvest of males would be the most appropriate harvest strategy for blue shark in the North Pacific.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".