Population structure inferred by local spatial autocorrelation: An example from an Amerindian tribal population
Bibliographic record
Abstract
Spatial autocorrelation (SA) methods were recently extended to detect local spatial autocorrelation (LSA) at individual localities. LSA statistics serve as useful indicators of local genetic population structure. We applied this method to 15 allele frequencies from 43 villages of a South American tribe, the Yanomama. Based on a network of links ≤51 km between neighboring villages, we calculated LSA statistics for Moran, Geary, and Getis-Ord coefficients. We also developed two new, rescaled indices of local SA. Local indicators of positive SA highlight villages surrounded by genetically similar near neighbors. Negative LSA statistics indicate sharp genetic differences from near neighbors. Markedly positive LSA was found for all 11 outlier villages. The most negatively LSA villages are in the central, densely connected cluster. The Getis-Ord coefficients of suitably transformed allele frequencies point to clusters of villages with unusually high or low allelic polymorphisms. The most homozygous villages are all in the four geographically isolated village clusters. The most polymorphic villages are all in the large, densely settled Yanomame dialect group. An ad hoc linguistic isolation index between neighboring villages showed that villages in isolated pairs and triplets have linguistically similar neighbors, whereas nine villages with notably negative LSA are all near dialect and kinship boundaries. The location of a village with respect to the graph structure of its neighborhood affects its LSA and genetic polymorphism. The implications of these findings for the population structure of the Yanomama are compatible with those from an earlier study of global SA in these villages. Am J Phys Anthropol, 2006. © 2005 Wiley-Liss, Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".