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Record W2150313622 · doi:10.1139/cjz-2012-0320

More sires may enhance offspring fitness in Northern Map Turtles (<i>Graptemys</i><i>geographica</i>)

2013· article· en· W2150313622 on OpenAlexaffvenueabout
Nicola Banger, Gabriel Blouin‐Demers, Grégory Bulté, Stephen C. Lougheed

Bibliographic record

VenueCanadian Journal of Zoology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsQueen's UniversityCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsBiologyOffspringHatchlingReproductive successPopulationPromiscuitySexual selectionZoologyDemographyEcologyHatchingGeneticsPregnancy

Abstract

fetched live from OpenAlex

Sexual selection theory predicts that males should be promiscuous to maximize their reproductive success, while females should be choosy. Yet females of many taxa often produce progeny sired by multiple males, indicating that promiscuity can be important for the reproductive success of females. Promiscuity may enhance the fitness of females if it increases the genetic quality, or the genetic variety, and thus the viability of their offspring. We quantified the number of sires per clutch in a population of Northern Map Turtles (Graptemys geographica (LeSueur, 1817)) in Lake Opinicon, Ontario, Canada, and tested whether the number of sires affects several metrics of viability in hatchlings. Based on the most conservative estimate, at least 71% of clutches in this population are sired by multiple males, but there was no evidence that larger clutches are sired by more males. Clutches sired by more males had higher hatching success and survival, but the differences were not statistically significant. We did not find any effect of the number of sires on hatchling morphology or locomotor performance. Collectively, our results partially support the hypothesis that promiscuity can increase the reproductive success of female Northern Map Turtles.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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.

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

Citations9
Published2013
Admission routes3
Has abstractyes

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