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Record W2761369007 · doi:10.1098/rspb.2017.1265

Genital interactions during simulated copulation among marine mammals

2017· article· en· W2761369007 on OpenAlexaff
Dara N. Orbach, Diane A. Kelly, Mauricio Solano, Patricia L. R. Brennan

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie University
FundersUniversity of Massachusetts Amherst
KeywordsEndocastBiologyPenisSex organMatingVaginaAnatomyZoologySexual selectionSkull

Abstract

fetched live from OpenAlex

Genitalia are morphologically variable across many taxa and in physical contact during intromission, but little is known about how variation in form correlates with function during copulation. Marine mammals offer important insights into the evolutionary forces that act on genital morphology because they have diverse genitalia and are adapted to aquatic living and mating. Cetaceans have a fibroelastic penis and muscular vaginal folds, while pinnipeds have a baculum and lack vaginal folds. We examined copulatory fit in naturally deceased marine mammals to identify anatomical landmarks in contact during copulation and the potential depth of penile penetration into the vagina. Excised penises were artificially inflated to erection with pressurized saline and compared with silicone vaginal endocasts and within excised vaginas in simulated copulation using high-resolution, diffusible iodine-based, contrast-enhanced computed tomography. We found evidence suggestive of both congruent and antagonistic genital coevolution, depending on the species. We suggest that sexual selection influences morphological shape. This study improves our understanding of how mechanical interactions during copulation influence the shape of genitalia and affect fertility, and has broad applications to other taxa and species conservation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
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 head0.032
GPT teacher head0.267
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations42
Published2017
Admission routes1
Has abstractyes

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