Bibliographic record
Abstract
Rissech et al. (J Forensic Sci 51 (2006) 213-229) described a method to estimate age-at-death of adult males using seven traits of the fused acetabulum. This study simplifies Rissech et al.'s technique and extends its application to adult females. Rissech et al.'s original scoring method was applied to a sample of 100 known-aged adults, three variables were selected based on stepwise multiple regression, and ages were collapsed into three broad ranges: young adult (17-39 years), middle adult (40-64 years), and old adult (65+ years). The revised method was applied to 249 new known-aged individuals from two other samples. To minimize observer bias, highlight the most critical traits, and encompass more age-related variation, unique digital renderings accompany morphological descriptions of age categories instead of photos. Three statistically significant characteristics highly correlated with age (P < 0.05) are capable of estimating age-at-death with 81% accuracy, both sexes combined. For misidentified individuals the tendency was to underestimate age. Results of both intraobserver error testing and inter-rater reliability demonstrated a moderate to substantial agreement in scoring between observers. When estimating the degree of development of features osteophyte development of the acetabular rim was the most inconsistent between observers. The revised acetabular method shows promise in estimating age for adults, particularly for those over the age of 65 years.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".