Assessing Virtual Anthropometric Measurements and Quantifying Their Relationship to Osteometric Measurements Using Computed Tomography Scans From an Online Database
Bibliographic record
Abstract
OBJECTIVES: Anthropometrics are a powerful tool for understanding bodily diversity. Using computed tomography (CT) scans from the New Mexico Decedent Image Database, we assess sources of methodological error that may complicate virtual anthropometric methods and quantify the comparability of anthropometric and osteometric measures. MATERIALS AND METHODS: Standard measurement protocols were adapted for virtual measurement. Intra- and interobserver measurement error, and error from changing CT scan thresholds were evaluated. Relationships between anthropometric and osteometric correlates were then evaluated using pairwise Fligner-Killeen tests on coefficients of variation (CVs). Biological codependence was explored using reduced major axis (RMA) regressions. RESULTS: Measurement and threshold errors were low. Breadth and circumferential measures had the largest measurement errors and were most impacted by different threshold choices. Linear measurements show no significant differences between anthropometric and osteometric definitions, while circumferential measures have significantly different CVs for the male (p = 0.02) and pooled groups (p = 0.01). Bi-iliocristal and bi-iliac breadths had significantly different CVs, except in the < 25 BMI group. RMA models reflect a positive linear relationship between paired measures that is stronger for linear (r ≥ 0.98) than circumferential (r = 0.82) measurements. Pelvic breadth shows the weakest relationship (r = 0.46). CONCLUSIONS: Anthropometric measures can be precisely defined in a replicable manner in virtual spaces, and linear measurements taken osteologically are comparable to those taken anthropometrically. However, circumferential measures are more impacted by threshold choice and soft tissue variation than linear measurements. Anthropometrically and osteometrically defined circumferential correlates should be compared with caution. Pelvic breadth measurements may only be comparable in individuals with BMIs < 25.
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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".