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Ancient Human DNA and African Population History

2022· reference-entry· en· W4280567045 on OpenAlexaff
Kendra Sirak, Elizabeth Sawchuk, Mary E. Prendergast

Bibliographic record

VenueOxford Research Encyclopedia of Anthropology · 2022
Typereference-entry
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAncient DNAGeographyPopulationEvolutionary biologyHuman evolutionHuman genetic variationArchaeologyVariation (astronomy)Archaeological recordHuman genomeHistoryGenomeBiologyDemographyGeneticsGeneSociology

Abstract

fetched live from OpenAlex

Abstract Ancient DNA has emerged as a powerful tool for investigating the human past and reconstructing the movements, mixtures, and adaptations that have structured genetic variation throughout human history. While the study of genome-wide ancient human DNA was initially restricted to regions with temperate climates, methodological breakthroughs have now extended the reach of ancient DNA analysis to parts of the world with hot and humid climates that are less conducive to biomolecular preservation. This includes Africa, where people harbor more genetic diversity than can be found anywhere else on the planet, reflecting deep and complex population histories. Since the first ancient African genome was published in 2015, the number of individuals with genome-wide data has increased to nearly 200, with greater coverage of diverse geographical, temporal, and cultural contexts. Ancient DNA sequences have revealed genetic variation in ancient African foragers that no longer exists in unadmixed form; illuminated how local-, regional-, and continental-scale demographic processes associated with the spread of food production and new technologies changed genetic landscapes; and discerned notable variation in interactions among people with distinct genetic ancestries, cultural practices, and, likely, languages. Despite an increasing number of studies focused on African ancient DNA, multiple regions and time periods have yet to be explored. Research to date has primarily focused on the past several thousand years in eastern and southern Africa, setting up northern, western, and central Africa, as well as deeper time periods, as key areas for future investigation. As ancient DNA research becomes increasingly integrated with anthropology and archaeology, it is advantageous to understand the basic methodological and analytical techniques, the types of questions that can be investigated, and the ways in which the discipline may continue to grow and evolve. Critically, the growth and evolution of ancient DNA research must include attention to the ethics of this work, both in African contexts and globally. In particular, it is essential that research is conducted in a way that minimizes the potential of harm to both the living and the dead. Scientists conducting ancient DNA research in Africa especially must also contend with structural challenges, including a lack of ancient DNA facilities on the continent, the extensive fragmentation of African heritage (including ancient human remains) among curating institutions worldwide, and the complexities of identifying descendant groups and other stakeholders in the wake of colonial and postcolonial disruptions and displacements. Ancient DNA research projects should be designed in a way that contributes to capacity building and the reduction of inequities between the Global North and South to ensure that the research benefits the people and communities with connections to the ancient individuals studied. While ensuring that future studies are rooted in ethical and equitable practices will require considerable collective action, ancient DNA research has already become an integral part of our understanding of African population history and will continue to shape our understanding of the African 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.359
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2022
Admission routes1
Has abstractyes

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