Apportioning archaic variants among modern populations
Bibliographic record
Abstract
The apportionment of human genetic diversity within and between populations has been measured to understand human relatedness and demographic history. Likewise, the distribution of archaic ancestry in modern populations can be leveraged to better understand the interaction between our species and its archaic relatives. Resolving the interactions between modern and archaic human populations can be difficult, as archaic variants in modern populations have been shaped by genetic drift, bottlenecks and gene flow. Here, we investigate the distribution of archaic variation in Eurasian populations. We find that archaic ancestry coverage at the individual- and population-level present distinct patterns in modern human populations: South Asians have nearly twice the number of population-unique archaic alleles compared with Europeans or East Asians, indicating that these populations experienced differing demographic and archaic admixture events. We confirm previous observations that East Asian individuals have more Neanderthal ancestry than European individuals, but surprisingly, when we compare the number of single nucleotide polymorphisms with archaic alleles found across a population, Europeans have more Neanderthal ancestry than East Asians. We compare these results to simulated models and conclude that these patterns are consistent with multiple admixture events between modern humans and Neanderthals. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".