Genetic composition of a Brazilian population: the footprint of the Gold Cycle
Bibliographic record
Abstract
Ancestry-informative markers (AIMs) are powerful tools for inferring the genetic composition of admixed populations. In this study, we determined the genetic ancestry of the Ouro Preto (Brazil) population and evaluated the association between ancestry and self-reported skin color. The genetic ancestry of 189 children and adolescents was estimated by genotyping 15 AIMs. The estimate of population admixture was determined using the Bayesian Markov Chain Monte Carlo (MCMC) method implemented in two different programs (STRUCTURE and ADMIXMAP). Volunteers self-reported their skin colors. The European ancestry contribution ranged from 0.503 to 0.539, the African contribution ranged from 0.333 to 0.425, and the Amerindian component ranged from 0.04 to 0.164. The relative contributions of African (P < 0.016) and European (P < 0.011) ancestry differed significantly among skin color groups, except between black and dark-brown groups. The population of Ouro Preto has a higher contribution of African ancestry compared to the mean for the southeast region of Brazil. Therefore, extrapolating the African ancestry contribution for southeastern Brazil to the Ouro Preto population would underestimate the actual value for this city. We also showed that self-reported skin color could be appropriate for describing the genetic structure of this particular population.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".