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Record W2899499303 · doi:10.1016/j.ijpp.2018.10.008

The people behind the samples: Biographical features of Past Hunter-Gatherers from KwaZulu-Natal who yielded aDNA

2018· article· en· W2899499303 on OpenAlexaff
Susan Pfeiffer, Lesley Harrington, Marlize Lombard

Bibliographic record

VenueInternational Journal of Paleopathology · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsBioarchaeologyAncient DNAPaleopathologyIron AgeGeographyArchaeologyGenealogyEvolutionary biologyHistoryBiologyDemographySociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.019
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.266
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2018
Admission routes1
Has abstractyes

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