Testing the accuracy of adult skeletal age estimation methods: original methods versus revised and newer methods.
Bibliographic record
Abstract
Accurately estimating adult skeletal age at death is a critical component of forensic case studies, archaeological fieldwork, and osteological research. This study applies 11 age estimation methods to a sample of 20 male skeletons of European descent (mean age 54.5 years, range 29-85) from the J.C.B. Grant Collection at the University of Toronto to assess the relative correctness (appropriate assignment to defined phases), reliability (inaccuracy and bias of estimations), and precision (reproducibility) of each method. Five original methods (Kunos et al., first rib; Iscan et al., fourth rib; Lovejoy et al., auricular surface; Todd, pubic symphysis; and Suchey-Brooks, pubic symphysis) were compared with six revised or newly developed methods (DiGangi et al., first rib; Hartnett, fourth rib; Passalacqua, sacrum; Buckberry and Chamberlain, auricular surface; Rouge-Maillart et al., acetabulum and auricular surface; and Hartnett, pubic symphysis). Results indicate that the newer methods correctly score individuals more consistently than the older methods; however, this may be attributed to the larger age ranges of their phases. The newer methods are also more reliable than the older methods: Buckberry and Chamberlain (6.54 inaccuracy, 2.24 bias) and Passalacqua (8.07 inaccuracy, -1.28 bias) rated highest. Newer methods had slightly lower rates of intra-observer error; newer and original methods had similar fair-to-moderate rates of inter-observer error. While newer methods may represent improvements to the field, they follow the trend of over-aging younger individuals and under-aging older individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.082 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".