Quantifying cortical bone in fragmentary archeological second metacarpals
Bibliographic record
Abstract
Abstract Objectives Skeletal variation in cortical bone thickness is an indicator of bone quality and health in archeological populations. Second metacarpal radiogrammetry, which measures cortical thickness at the shaft midpoint, is traditionally used to evaluate bone loss in bioarcheological and some clinical contexts. However fragmentary elements are regularly omitted because the midpoint cannot be determined. This methodological limitation reduces sample sizes and biases them against individuals prone to fracture, such as older individuals with low bone mass. This study introduces a new technique for measuring cortical bone in second metacarpals, the “Region of Interest” (ROI) method, which quantifies bone in archeological remains with less‐than‐ideal preservation while accounting for cortical heterogeneity. Materials and methods The ROI method was adapted from digital X‐ray radiogrammetry (DXR), a clinical method used to estimate bone mineral density, and tested using second metacarpals from Middenbeemster, Netherlands, a 19th century known age and sex skeletal collection. The ROI method quantifies cortical bone area within a 1.9 cm‐long, mid‐diaphyseal region, standardized for body size differences using total area (CAIROI). CAIROI values were compared to traditional radiogrammetric cortical indices (CI) to assess the method's ability to identify age‐related bone loss. Results CAIROI values have high intra‐ and interobserver replicability and are strongly and significantly correlated with CI values for both males (r[n = 39] = 0.906, p = 0.000) and females (r[n = 58] = 0.925, p = 0.000). Conclusion The ROI method complements traditional radiogrammetry analyses and provides a reliable way to quantify cortical bone in incomplete second metacarpals, thereby maximizing sample sizes, allowing patterns in bone acquisition and loss to be more comprehensively depicted in archeological assemblages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".