A re-examination of the age-at-death distribution of Indian Knoll
Bibliographic record
Abstract
Introduction A majority of prior paleodemographic studies have focused on the estimation of population structure utilizing individual age range estimates derived from a variety of age indicators and compiled into a life table (Johnston and Snow 1961; Weiss 1973; Mensforth 1990). General population parameters from the life table are then compared among populations or with model mortality schedules (e.g., Coale and Demeny 1966). Recent research has demonstrated that age-at-death distributions derived from these types of age estimation method are biased as a result of an a priori assumption equating the age-at-death distributions of the reference and skeletal samples (Bocquet-Appel and Masset 1982, 1996; Konigsberg and Frankenberg 1992, 1994). In this chapter, we will provide a case study based on an extension of the statistical methods detailed in Konigsberg and Herrmann (Chapter 11, this volume) using pelvic age indicator data from the large Archaic skeletal sample from Indian Knoll (15Oh2), Kentucky. This well-preserved skeletal series offers a unique opportunity to test these new methods. We compare the age-at-death distribution derived from this new approach with mortality data collected by several researchers from the Indian Knoll series. Our comparison illustrates differences between the earlier techniques, specifically life table based analyses, and our new method, which utilizes modeled hazard parameters and unbiased age estimates. Indian Knoll history The Indian Knoll skeletal series represents over 1100 individuals. The burial sample is one of the largest North American hunter–gatherer skeletal collections from a single site.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".