Gene flow, adaptive population divergence and comparative population structure across loci
Bibliographic record
Abstract
Summary Many recent studies have sought to identify targets of diversifying selection by testing the neutral expectation that all loci will show similar levels of population divergence ( F st ). Contrasts between quantitative traits ( Q st ) and molecular markers ( F st ) suggest that quantitative traits typically diverge in response to local selection pressures more than do individual genes. Coalescence theory makes it possible to simulate the distribution of F st and Q st expected under neutrality for many situations, including nonequilibrium conditions. Such simulations show that a very high variance of F st and Q st are expected under neutrality, making it difficult to draw firm conclusions about the action of selection on individual loci or traits. Recent quantitative genetic theory shows that, under diversifying selection on quantitative traits, covariances (linkage disequilibrium) among allele frequencies at underlying additive loci contribute a substantial fraction of the among‐population trait variance. Thus, adaptive trait divergence can be accomplished, with limited divergence of allele frequencies. However, the contribution of covariances among loci to the divergence of traits depends upon there being multiple loci underlying quantitative trait variation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".