Bibliographic record
Abstract
In human females, the bony pelvis must find a balance between being small (narrow) for efficient bipedal locomotion, and being large to accommodate a relatively large newborn. It has been shown that within a given population, taller/larger-bodied women have larger pelvic canals. This study investigates whether in a population where small body size is the norm, pelvic geometry (size and shape), on average, shows accommodation to protect the obstetric canal. Osteometric data were collected from the pelves, femora, and clavicles (body size indicators) of adult skeletons representing a range of adult body size. Samples include Holocene Later Stone Age (LSA) foragers from southern Africa (n = 28 females, 31 males), Portuguese from the Coimbra-identified skeletal collection (CISC) (n = 40 females, 40 males) and European-Americans from the Hamann-Todd osteological collection (H-T) (n = 40 females, 40 males). Patterns of sexual dimorphism are similar in the samples. Univariate and multivariate analyses of raw and Mosimann shape-variables indicate that compared to the CISC and H-T females, the LSA females have relatively large midplane and outlet canal planes (particularly posterior and A-P lengths). The LSA males also follow this pattern, although with absolutely smaller pelves in multivariate space. The CISC females, who have equally small stature, but larger body mass, do not show the same type of pelvic canal size and shape accommodation. The results suggest that adaptive allometric modeling in at least some small-bodied populations protects the obstetric canal. These findings support the use of population-specific attributes in the clinical evaluation of obstetric risk.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".