Variation in cortical bone histology within the human femur and its impact on estimating age at death
Bibliographic record
Abstract
Histological methods for the estimation of age at death using cortical bone are based on the evaluation of microstructural changes over time. Since histological analysis is a destructive method, most techniques attempt to limit the amount of cortical bone needed for analysis. Sample location, however, can have a significant effect on the accuracy of these methods. Furthermore, research demonstrates that both intersection and intrasection variation is present at the midshaft of the femur, which is the primary location for estimating age at death in humans. This research determines the extent of regional variation within the adult human femur and its effect on age estimation. Secondary osteon lamellae and Haversian canal ratio and cortical thickness were quantified. Thompson's All Males Left Femur regression equation was used to estimate age. Results show that significant regional variation occurs in the estimated ages derived from the posterior aspect of the femoral shaft and significant intrasection variation occurs in age estimates from the mid and mid-distal cross-sections. Thus, the inter and intrasection variation that occurs in bone remodeling within the femoral cortex has the potential to produce significant differences amongst age estimates taken from various femoral diaphyseal locations compared to the age estimated from the standard location used in Thompson's core method (1978). The results indicate that the use of this histological method is dependant on the ability to correctly identify the four anatomical locations, but the extracted core used for age estimation is not necessarily confined to the anterior midshaft.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".