Conservation and management of exploited shark populations based on reproductive value
Bibliographic record
Abstract
Several life history traits of sharks result in juveniles being particularly vulnerable to exploitation. However, population level impacts of harvests on juvenile sharks have not been well quantified. This paper examines a range of harvest strategies, including those targeting juveniles. Reproductive value and yield per recruit are used to compare the harvests, which are represented by Leslie matrix models with a harvest matrix. Two species are used as examples: the short-lived Rhizoprionodon taylori and the long-lived Squalus acanthias. Harvests that maintain a stationary population size cause reproductive values to change in opposing ways, but they remove equal fractions of the population's reproductive potential. A new theorem gives population growth as a function of the fraction of reproductive potential removed by a harvest, a relationship useful for comparing harvests on juveniles and adults. Stochastic projections indicate that the risk of depletion is associated with the fraction of reproductive potential removed annually, a measure which encompasses the information in both the selectivity and the rate of fishing mortality. These results indicate the value of focusing conservation efforts on preserving reproductive potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".