Body size estimation of small‐bodied humans: Applicability of current methods
Bibliographic record
Abstract
Body size (stature and mass) estimates are integral to understanding the lifeways of past populations.Body size estimation of an archaeological skeletal sample can be problematic when the body size or proportions of the population are distinctive. One such population is that of the Holocene Later Stone Age (LSA) of southern Africa, in which small stature (mean femoral length = 407 mm, n = 52) and narrow pelves (mean bi-iliac breadth = 210 mm, n = 50) produce a distinctive adult body size/shape, making it difficult to identify appropriate body size estimation methods. Material culture, morphology, and culture history link the Later Stone Age people with the descendant population collectively known as the Khoe-San. Stature estimates based on skeletal "anatomical" linear measures (the Fully method) and on long bone length are compared, along with body mass estimates derived from "morphometric" (bi-iliac breath/stature) and "biomechanical" (femoral head diameter) methods, in a LSA adult skeletal sample (n = 52) from the from coastal and near-coastal regions of South Africa. Indices of sexual dimorphism (ISD) for each method are compared with data from living populations. Fully anatomical stature is most congruent with Olivier's femur + tibia method, although both produce low ISD. McHenry's femoral head body mass formula produces estimates most consistent with the bi-iliac breadth/stature method for the females, although the males display higher degrees of disagreement among methods. These results highlight the need for formulae derived from reference samples from a wider range of body sizes to improve the reliability of existing methods.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".