Testing landmark redundancy for sex‐based shape analysis of the adult human os coxa
Bibliographic record
Abstract
OBJECTIVES: To test the individual effectiveness of common landmarks used in sex estimation of whole adult coxal bones in sex-based shape analysis and propose a method to determine how many principal components of sex-based shape to include for discriminant function analysis. METHODS: Three-dimensional models (NextEngine desktop laser scanner) of left and right os coxae from 396 individuals (William Bass Skeletal Collection, Forensic Anthropology Centre, University of Tennessee, Knoxville, TN) were subjected to shape analysis using 32 landmarks (Landmark 3.6, Institute for Data Analysis and Visualization). Each landmark was individually removed and subjected to a new principal component analysis to identify the effect omitting a landmark has on PC1/PC2 ordination. Landmarks that poorly discriminated sex-based shape were considered redundant for analysis on sex estimation. RESULTS: This study identified 17 landmarks that represent sex-based shape of right and left coxal bones most effectively, these are: the anterior superior iliac spine; posterior superior iliac spine; posterior inferior iliac spine; iliac crest; apex of the auricular surface; greater sciatic notch; ischial spine; superior, inferior and distal points on ischial tuberosity; superior, inferior and midpoint on the symphyseal face; arcuate eminence; ischiopubic ramus; posterosuperior and anterosuperior points on the acetabular rim. The first and second PCs of the 17-landmark configuration correctly predicted sex in 98.5% of cases; better than a 32-landmark configuration (96%) and better than previous landmark studies on whole coxal bone sex-based shape. CONCLUSIONS: These 17 landmarks represent more meaningful data for sex-based shape analysis in PC1 and 2 and concentrate meaningful sex-based shape data to the first five PCs that make up over 50% of the total shape variance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".