Phenotypic approaches for understanding patterns of intracemetery biological variation
Bibliographic record
Abstract
This paper reviews studies of phenotypic inheritance and microevolutionary processes in archaeological populations using data on cranial and dental phenotypic variation, often referred to as paleogenetics or biodistance analysis. The estimation of biological distances between populations, or among individuals within populations, is one component of bioarchaeological research on past populations. In this overview, five approaches that focus on morphological variation within cemeteries are summarized: kinship and cemetery structure analysis, postmarital residence analysis, sample aggregate phenotypic variability, temporal microchronology, and age-structured phenotypic variation. Previous research, theoretical justifications, and methods are outlined for each topic. Case studies are presented that illustrate these theoretical and methodological bases, as well as demonstrate the kinds of inferences possible using these approaches. Kinship and cemetery structure analysis seeks to identify the members of family groups within larger cemeteries or determine whether cemeteries were kin-structured. Analysis of sex-specific phenotypic variation allows estimation of postmarital residence practices, which is important for understanding other aspects of prehistoric social organization. Analysis of aggregate phenotypic variability can be used to infer site formation processes or cemetery catchment area. The study of temporal microchronologies can be used to evaluate provisional archaeological chronologies or study microevolutionary processes such as adaptive selection or changing patterns of gene flow. Finally, age-structured phenotypic variation can be reflective of selection processes within populations or it can be used as a measure of morbidity, growth arrest, and early mortality within past populations. Use of phenotypic data as a genotypic proxy is theoretically sound, even at small scales of analysis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".