Bibliographic record
Abstract
The human first rib is relatively easy to identify and is often preserved, in comparison with elements such as the fourth rib and pubic symphysis. Therefore it is potentially a valuable skeletal element for estimating age in forensic and archaeological contexts. A method of adult age estimation using the first rib (Kunos et al., 1999) is tested on a sample of known age skeletons from the J.C.B. Grant Collection (n = 29, mean age = 55.7 years). The high correlation coefficient (r = 0.69) and moderate coefficient of determination (r2 = 0.47) demonstrate agreement between the known and estimated ages, suggesting that the first rib demonstates morphological changes with age. The inaccuracy and bias are high (all ages inaccuracy = 10.4 years, bias = 4.7 years) but comparable to several other age estimation methods in common use. Although the results are not as good for younger age categories (< 50 years: inaccuracy and bias rank ninth of nine age estimation methods), the inaccuracy and bias for the older age categories are relatively low (60 + years inaccuracy = 8.9 years, ranks third out of nine; bias = − 5.8 years, ranks first out of nine) compared with other age estimation methods. The first rib method is reasonably precise (93% of individuals fall within the limits of agreement of the mean difference between two trials). The first rib method is therefore a useful addition to the methods available for biological profile reconstructions from skeletal remains, especially if it is suspected that the remains represent an older individual. Copyright © 2005 John Wiley & Sons, Ltd.
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 | 0.161 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".