On the Apportionment of Archaic Human Diversity
Bibliographic record
Abstract
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, and the impact of natural selection on archaic segments of the human genome. Resolving these interactions can be difficult, as archaic variants in modern populations have also been shaped by genetic drift, bottlenecks, and gene flow. Here, we investigate the apportionment of archaic variation in Eurasian populations. We find that archaic genome coverage at the individual- and population-level present unique patterns in modern human population: South Asians have an elevated count of population-unique archaic SNPs, and Europeans and East Asians have a higher degree of archaic SNP sharing, indicating that population demography and archaic admixture events had distinct effects in these populations. We confirm previous observations that East Asians have more Neanderthal ancestry than Europeans at an individual level, but surprisingly Europeans have more Neandertal ancestry at a population level. In comparing these results to our simulated models, we conclude that these patterns likely reflect a complex series of interactions between modern humans and archaic populations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".