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Record W2168161020 · doi:10.1139/f07-005

Frequency of multiple paternity in an unexploited tropical population of sandbar sharks (<i>Carcharhinus plumbeus</i>)

2007· article· en· W2168161020 on OpenAlexvenueno aff
Toby S. Daly‐Engel, R. Dean Grubbs, Brian W. Bowen, Robert J. Toonen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsnot available
Fundersnot available
KeywordsCarcharhinusBiologyPopulationFisheryMatingEcologyMating systemOverexploitationZoologyDemography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.231
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2007
Admission routes1
Has abstractyes

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