Rib Histomorphometry for Adult Age Estimation
Bibliographic record
Abstract
Estimating the age at death in the adult skeleton is problematic owing to the biological variability in morphological age indicators and the differential response to environmental factors over an individual's life. It is becoming increasingly important for anthropologists to improve age estimates through the use of multiple age indicators and various modalities of assessment (e.g., macroscopic, microscopic, and radiological). Lack of instructional texts describing how to prepare histological samples and evaluate bone under the microscope has been a restricting factor in the widespread use of current histological methods within the field of forensic anthropology. The limited use of histological methods for age estimation often lies in the misunderstanding that the preparation and evaluation of cortical bone thin sections is a highly technical and an expensive endeavor. Like any method of age estimation, the researcher/practitioner must be guided through the analytical process to ensure reliable and repeatable results. This chapter provides a step-by-step instructional guide in the preparation and evaluation of histological samples removed from the sixth rib for histological age estimation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".