The people behind the samples: Biographical features of Past Hunter-Gatherers from KwaZulu-Natal who yielded aDNA
Bibliographic record
Abstract
PURPOSE: Skeletons sampled for ancient human DNA analysis are sometimes complete enough to provide information about the lives of the people they represent. We focus on three Later Stone Age skeletons, ca. 2000 B.P., from coastal KwaZulu-Natal, South Africa, whose ancient genomes have been sequenced (Schlebusch et al., 2017). METHODS: Bioarchaeological approaches are integrated with aDNA information. RESULTS: All skeletons are male. Dental development shows that the boy, with prominent cribra orbitalia, died at age 6-7 years. Two men show cranial and spinal trauma, extensive tooth wear, plus mild cribra orbitalia in one. CONCLUSIONS: Dental wear and trauma of the adults are consistent with hunter-gatherer lives. Even partial aDNA evidence contributes to sex determination. Parasitic infection such as schistosomiasis is the best-fit cause for the child's anemia in this case. CONTRIBUTION TO KNOWLEDGE: The convergence of genomic and bioarchaeological approaches expands our knowledge of the past lives of a boy and two men whose lives as hunter-gatherers included episodes of trauma and disease. LIMITATIONS: The skeletons are incomplete, in variable condition, and from poorly characterized local cultural contexts. SUGGESTIONS FOR FURTHER RESEARCH: Thorough osteobiographic analysis should accompany paleogenomic investigations. Such disciplinary collaboration enriches our understanding of the human past.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".