Differences between biological and chronological age‐at‐death in human skeletal remains: A change of perspective
Bibliographic record
Abstract
OBJECTIVES: This analysis seeks to determine whether differences between real and estimated chronological age (CA) with biological age (BA) in skeletal individuals reflect variability in aging. MATERIAL AND METHODS: A total of 87 individuals of two samples, ranging from 20 to 94 years old, were analyzed. One, partially documented, belongs to a Mexican skeletal collection dating to the 20th century; the other is an assemblage of prehispanic individuals from different archaeological sites. In all specimens, the tooth annulation method (TCA) was applied to estimate CA, while-excluding individuals older than 80 years-auricular surface (AS) and pubic symphysis (PS) methods were used to estimate BA. Statistical analyses were conducted to identify correlations and significance of the differences between CA vs. TCA, CA vs. AS/PS, TCA vs. AS/PS. Sex of individuals was assessed for its influence in aging. RESULTS: The use of TCA to estimate CA was successful for most individuals. A strong correlation was found between CA vs. TCA, CA vs. AS/PS, TCA vs. AS/PS and their differences were significant but variation in these were found when assessed by separate age groups. Sex did not influence such differences. DISCUSSION: TCA can be used to estimate CA and its differences with BA, being less than 10 years, are similar to those found in living populations. Differences between CA and BA are due to intra-population variability, which could be the consequence of individual differences in aging. More research is needed to have confidence that under- and overestimations of BA are indicators of aging variability at the level of the individual.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".