Ancient Human DNA and African Population History
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».