A Method to Interpolate Osteon Volume Designed for Histological Age Estimation Research
Bibliographic record
Abstract
Aging adult skeletal material is a crucial component of building the biological profile of unknown skeletal remains, but many macro- and microscopic methods have challenges regarding accuracy, precision, and replicability. This study developed a volumetric method to visualize and quantify histological remodeling events in three dimensions, using a two-dimensional serialized approach that applied circular polarizing microscopy and geographic information systems protocols. This approach was designed as a tool to extend current histological aging methodologies. Three serial transverse sections were obtained from a human femoral midshaft. A total sample size of 6847 complete osteons from the three sections was identified; 1229 osteons connected between all sections. The volume of all connected osteons was interpolated using ArcGIS area calculations and truncated cone geometric functions. Each section was divided into octants, and two random samples of 100 and of 30 connected osteons from each octant were generated. Osteon volume was compared between the octants for each random sample using ANOVA. Results indicated that the medial aspect had relative uniformity in osteon volume, whereas the lateral aspect showed high variability. The anterolateral-lateral octant had significantly smaller osteon volume, whereas the posterior-posterolateral octant had significantly larger osteon volume. Results also indicated that a minimum of 100 osteons is statistically more robust and more representative of normal osteon distribution and volume; the use of 30 osteons is insufficient. This research has demonstrated that osteon volume can be interpolated using spatial geometry and GIS applications and may be a tool to incorporate into adult age-at-death estimation techniques.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".