Cortical bone histomorphology of known‐age skeletons from the <scp>K</scp>irsten collection, <scp>S</scp>tellenbosch university, <scp>S</scp>outh <scp>A</scp>frica
Bibliographic record
Abstract
OBJECTIVES: Normal human bone tissue changes predictably as adults get older, but substantial variability in pattern and pace remains unexplained. Information is needed regarding the characteristics of histological variables across diverse human populations. METHODS: Undecalcified thin sections from mid-thoracic ribs of 213 skeletons (138 M, 75 F, 17-82 years, mean age 48 years), are used to explore the efficacy of an established age-at-death estimation method and methodological approach (Cho et al.: J Forensic Sci 47 (2002) 12-18) and expand on it. The ribs are an age-balanced sample taken from skeletonized cadavers collected from 1967 to 1999 in South Africa, each with recorded sex, age, cause of death and government-defined population group (129 "Colored," 49 "Black," 35 "White"). RESULTS: The Ethnicity Unknown equation performs better than those developed for European-Americans and African-Americans, in terms of accuracy and bias. A new equation based solely on the study sample does not improve accuracy. Osteon population densities (OPD) show predicted values, yet secondary osteon areas (On.Ar) are smaller than expected for non-Black subgroups. Relative cortical area (Ct.Ar/Tt.Ar) is low among non-Whites. CONCLUSIONS: Results from this highly diverse sample show that population-specific equations do not increase estimate precision. While within the published range of error for the method (±24.44 years), results demonstrate a systematic under-aging of young adults and over-aging of older adults. The regression approach is inappropriate. The field needs fresh approaches to statistical treatment and to factors behind cortical bone remodeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.115 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".