A program to compare genetic differentiation statistics across loci using resampling of individuals and loci
Bibliographic record
Abstract
Comparisons of genetic differentiation across populations based on different loci can provide insight into the evolutionary patterns acting on various regions of genomes. Here, we develop a program to statistically compare population genetic differentiation statistics (F(ST) or G'(ST) ) calculated from different loci. The program employs a routine that resamples either or both of individuals and loci and calculates a bootstrap confidence interval in the statistics. Resampling individuals is important when fewer than 25 individuals are sampled per population and when confidence intervals are required for individual loci. Resampling loci provides confidence intervals for sets of loci, such as a set presumed to be neutral, but can be anticonservative if fewer than 20 loci are analysed. We demonstrate the program using previously published data on the genetic differentiation at a major histocompatibility complex locus and at microsatellite loci across 10 populations of the guppy (Poecilia reticulata).
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".