The Craniometric Value of Foramen Magnum Length & Nasal Breadth. A Statistical Exploration of the Significance of Variation Across Sex & Populations.
Bibliographic record
Abstract
Various skeletal features, ranging from the cranium to the post-cranium, vary significantly across populations and sex, resulting from complex evolutionary histories that involve biological and cultural differences between the sexes as well as adaptations to local environment and lifestyle across populations. The cranium is particularly reliable as both a sex and population variant; however, although craniofacial features have been supported as variants in the literature, cranial landmarks from other regions, such as foramen magnum length, is often overlooked. This study employs statistical tests to assess the variation of foramen magnum length and nasal breadth, particularly interesting for its function in respiration and possibility for adaptation to climate, between sex and population. A two-way ANOVA shows significant variation between sex and population for foramen magnum length [F(1) = 86.97, p < 0.01; F(6) = 48.12, p < 0.01] and nasal breadth [F(1) = 62.61, p < 0.01; F(6) = 105.4, p < 0.01]. Pearson’s linear correlation coefficient shows a moderate relationship between foramen magnum length and population latitude [r(5) = 0.57335, p = 0.1784] and nasal breadth and population latitude [r(5) = -0.48341, p = 0.27176]. However, with an outlier excluded, Pearson’s correlation coefficient shows a statistically significant positive correlation between foramen magnum length and latitude [r(4) = 0.94423, p = 0.004578]. The results conclude that there is significant variation in foramen magnum length and nasal breadth across sex and population. This study suggests that foramen magnum length has potential as a sex and population variant upon further research.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".