Health status by gender, hair color, and eye color: Red-haired women are the most divergent
Bibliographic record
Abstract
Red hair is associated in women with pain sensitivity. This medical condition, and perhaps others, seems facilitated by the combination of being red-haired and female. We tested this hypothesis by questioning a large sample of Czech and Slovak respondents about the natural redness and darkness of their hair, their natural eye color, their physical and mental health (24 categories), and other personal attributes (height, weight, number of children, lifelong number of sexual partners, frequency of smoking). Red-haired women did worse than other women in ten health categories and better in only three, being particularly prone to colorectal, cervical, uterine, and ovarian cancer. Red-haired men showed a balanced pattern, doing better than other men in three health categories and worse in three. Number of children was the only category where both male and female redheads did better than other respondents. We also confirmed earlier findings that red hair is naturally more frequent in women than in men. Of the 'new' hair and eye colors, red hair diverges the most from the ancestral state of black hair and brown eyes, being the most sexually dimorphic variant not only in population frequency but also in health status. This divergent health status may have one or more causes: direct effects of red hair pigments (pheomelanins) or their by-products; effects of other genes that show linkage with genes involved in pheomelanin production; excessive prenatal exposure to estrogen (which facilitates expression of red hair during fetal development and which, at high levels, may cause health problems later in life); evolutionary recentness of red hair and corresponding lack of time to correct negative side effects; or genetic incompatibilities associated with the allele Val92Met, which seems to be of Neanderthal origin and is one of the alleles that can cause red hair.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".