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Record W1994488975 · doi:10.1375/twin.11.2.204

Sex Ratio in Sibships With Twins

2008· article· en· W1994488975 on OpenAlexaboutno aff
Johan Fellman, Aldur W. Eriksson

Bibliographic record

VenueTwin Research and Human Genetics · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsSex ratioDemographyStatisticsQuarter (Canadian coin)GermanMathematicsGeographyPopulationSociology

Abstract

fetched live from OpenAlex

In national birth registers of Caucasians, the secondary sex ratio, that is, the number of boys per 100 girls at birth, is almost constant at 106. Variations other than random variation have been noted, and attention is being paid to identifying presumptive influential factors. Studies of the influence of different factors have, however, yielded meagre results. An effective means of identifying discrepancies is to investigate birth data compiled into sibships of different sizes. Assuming no inter- or intra-maternal variations, the distributions of the sex composition are binomial. Varying parental tendencies for a specific sex result in discrepancies from the binomial distribution. Over a century ago, the German scientists Geissler and Lommatzsch analyzed the vital statistics of Saxony, including twin maternities, for the last quarter of the 19th century. They considered sibships ending with twin sets. Their hypothesis was that in sibships ending with male-male twin pairs, the sex ratio among previous births is higher than normal, while in sibships with female-female twin pairs, the sex ratio is lower than normal. If the sibship ended with a male-female pair, then the sex ratio is almost normal. Consequently, a same-sex twin set indicated, in general, deviations in the sex ratio among the sibs within the sibship. Our analyzes of their data yielded statistically significant results that support their statements.

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 score0.049
Threshold uncertainty score0.967

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 studies0.0010.001
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 head0.252
GPT teacher head0.415
Teacher spread0.163 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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