Bibliographic record
Abstract
Abstract The fixation probability of an allele is the probability that it will eventually be the ancestor of all the alleles within a population at that locus. Population genetics theory has demonstrated that the probability of fixation is approximately proportional to the selection coefficient of a weak beneficial mutation, because such mutations are susceptible to stochastic loss while being rare despite their advantage. The time to fixation is the number of generations that it takes for an allele to progress from its initial frequency to fixation. This time is inversely proportional to the selection coefficient of a beneficial allele. Interestingly, though deleterious alleles are much less likely to fix, the time that they take to do so is, on average, the same as for a beneficial allele with the same magnitude of selective effect. Key Concepts: The fixation probability of an allele is the probability that it will eventually be the ancestor of all the alleles within a population at that locus. Even beneficial mutations may not fix within a population. The fixation probability of a beneficial allele is approximately proportional to its selection coefficient. Deleterious mutations are unlikely to fix, but they can fix if their selective disadvantage is sufficiently weak and the population size sufficiently small. The time for a new allele to fix within a population is inversely proportional to the magnitude of selection for both beneficial and deleterious alleles.
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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".