The impact of epistatic selection on the genomic traces of selection
Bibliographic record
Abstract
The rapid accumulation of genomic data has led to an explosion of studies searching for signals of past selection left within DNA sequences. Yet the majority of theoretical studies investigating the traces of selection have assumed a simple form of selection, without interactions among selectively fixed sites. Fitness interactions-'epistasis'-are commonplace, however, and take on a myriad of forms (Whitlock et al. 1995; Segrèet al. 2005; Phillips 2008). It is thus important to determine how such epistasis would influence selective sweeps. On p. 5018 of this issue, Takahasi (2009) explores the effect of epistasis on genetic variation neighbouring two sites that interact in determining fitness, finding that such epistasis has a dramatic impact on the genetic variability in regions surrounding the interacting sites.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".