Bibliographic record
Abstract
Abstract Genetic drift is the random change in allele frequencies by the chance success of some alleles relative to others. Genetic drift is more important in small populations, where chance plays stronger role. If the population size is small enough relative to the strength of selection, genetic drift can sometimes cause slightly deleterious alleles to rise in frequency or beneficial alleles to be lost from a population. Drift can lead to the fixation or loss of alleles, and therefore drift can contribute to the loss of genetic variation. As a consequence, genetic drift in small populations is a source of concern for the future evolutionary potential for some endangered species. Key Concepts: Alleles may increase or decrease in frequency by chance. The effects of chance on allele frequency change are most pronounced in small populations. Genetic drift tends to lead to fixation or loss of alleles over time, and therefore contributes to the loss of genetic variation. If the population size is small enough relative to the strength of selection, genetic drift can cause the fixation of deleterious alleles or loss of beneficial alleles. Genetic drift can cause genetic divergence between species or populations. Most genetic differences between species are probably due to genetic drift. Genetic drift is nonadaptive and nondirectional.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.135 | 0.051 |
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".