Brief communication: The relation between standard error of the estimate and sample size of histomorphometric aging methods
Bibliographic record
Abstract
Histomorphometric aging methods report varying degrees of precision, measured through Standard Error of the Estimate (SEE). These techniques have been developed from variable samples sizes (n) and the impact of n on reported aging precision has not been rigorously examined in the anthropological literature. This brief communication explores the relation between n and SEE through a review of the literature (abstracts, articles, book chapters, theses, and dissertations), predictions based upon sampling theory and a simulation. Published SEE values for age prediction, derived from 40 studies, range from 1.51 to 16.48 years (mean 8.63; sd: 3.81 years). In general, these values are widely distributed for smaller samples and the distribution narrows as n increases--a pattern expected from sampling theory. For the two studies that have samples in excess of 200 individuals, the SEE values are very similar (10.08 and 11.10 years) with a mean of 10.59 years. Assuming this mean value is a 'true' characterization of the error at the population level, the 95% confidence intervals for SEE values from samples of 10, 50, and 150 individuals are on the order of ± 4.2, 1.7, and 1.0 years, respectively. While numerous sources of variation potentially affect the precision of different methods, the impact of sample size cannot be overlooked. The uncertainty associated with SEE values derived from smaller samples complicates the comparison of approaches based upon different methodology and/or skeletal elements. Meaningful comparisons require larger samples than have frequently been used and should ideally be based upon standardized samples.
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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 | 0.030 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
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".