Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".