Frequency of multiple paternity in an unexploited tropical population of sandbar sharks (<i>Carcharhinus plumbeus</i>)
Bibliographic record
Abstract
Elasmobranch mating systems have received growing attention in the past few years because of worldwide overexploitation of shark populations. Few studies to date have examined mating systems in sharks because of difficulty in sampling. The sandbar shark (Carcharhinus plumbeus) is heavily harvested around the world and is the dominant species in the main commercial fishery for large coastal sharks in the United States. In contrast, Hawaii hosts one of the few unexploited populations of sandbar sharks and represents an opportunity to gather data on the reproductive biology of a vulnerable shark species without the confounding effects of fishing mortality. We examined the frequency of multiple paternity in Hawaiian sandbar sharks using 130 individuals (20 gravid females with three–eight pups each per litter) surveyed with six polymorphic microsatellite loci and determined that 8 of the 20 litters (40%) were multiple- sired. A Bayesian approach estimated the frequency of multiple mating in this population at 43.8%, with a 95% confidence interval of 23%–63%. We conclude that multiple paternity and genetic monogamy occur with roughly equal frequency in the Hawaiian sandbar shark population. This study may serve as groundwork for understanding the impact of commercial fishing pressure on elasmobranch mating systems.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".