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Record W2047551920 · doi:10.1002/jez.a.317

Prenatal sex ratios and expression of sexually dimorphic traits in three snake species

2006· article· en· W2047551920 on OpenAlexaff
Patrick J. Weatherhead, Kelley J. Kissner, Sophie J. Sommerer

Bibliographic record

VenueJournal of Experimental Zoology Part A Comparative Experimental Biology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsCarleton University
Fundersnot available
KeywordsSexual dimorphismThamnophis sirtalisBiologyLizardLitterZoologySex ratioSex characteristicsEcologyEndocrinologyDemographyPopulation

Abstract

fetched live from OpenAlex

Variation in intrauterine exposure to hormones associated with variation in the sex of litter mates has well-established and far-reaching effects on sexual development in some mammals. Research on this phenomenon in reptiles is scant, but suggests that lizards may follow the mammalian model whereas snakes may be affected differently. We examined sex-specific expression of four sexually dimorphic traits (tail length, head length, ventral scale count, swimming speed) in three species of snakes (Nerodia sipedon, Thamnophis sirtalis, T. sauritus) relative to litter sex ratios. We found little evidence that traits in either sex were masculinized or feminized in response to variation in litter sex ratio. The one significant result appeared best explained as a statistical artifact attributable to a single litter. Our results indicate that snakes are different from the one lizard studied to date. Unlike previous suggestions that prenatal hormonal mechanisms operate differently in snakes and lizards, however, the difference appears to be that development of sexually dimorphic traits in lizards is affected by litter sex ratios whereas in snakes it is not.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.032
GPT teacher head0.283
Teacher spread0.252 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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Same venueJournal of Experimental Zoology Part A Comparative Experimental BiologySame topicWildlife Ecology and ConservationFrench-language works237,207