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Digit ratio (2D:4D) and behavioral differences between inbred mouse strains

2004· article· en· W2165940314 on OpenAlexaff
Allison A. Bailey, Douglas Wahłsten, Peter L. Hurd

Bibliographic record

VenueGenes Brain & Behavior · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSexual Differentiation and Disorders
Canadian institutionsUniversity of WindsorUniversity of Alberta
FundersNational Institute on Alcohol Abuse and AlcoholismDirectorate for Biological SciencesNational Institutes of Health
KeywordsDigit ratioNumerical digitTraitBiologyInbred strainPsychologyTestosterone (patch)Developmental psychologyGeneticsMathematicsArithmetic

Abstract

fetched live from OpenAlex

Digit ratio (2D:4D) is a trait, which is sexually differentiated in a variety of species. In humans, males typically have shorter second digits (2Ds) (index fingers) compared to fourth digits (4Ds) (ring fingers) whereas females' fingers are more equal in length. Smaller, more masculine, digit ratios are thought to be associated with higher prenatal testosterone levels, greater sensitivity to prenatal androgens or both. Men with more masculine digit ratios have shown increased ability, achievement and speed in sports and tend to report that they are more physically aggressive. Previous research has shown the same sexually differentiated pattern in the hind paws of laboratory mice as in human hands, males have lower 2D:4D than females. We measured hind paw digit ratio in mice of eight inbred strains. These measurements were made while blind to strain, sex and whether the paw was from the left or right side. We found large differences in digit ratio between the strains and suggest that inbred mice are a promising system for investigating the correlation between digit ratio and behavioral traits.

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)0.001
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.304
Teacher spread0.267 · 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

Citations37
Published2004
Admission routes1
Has abstractyes

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