Bibliographic record
Abstract
In the previous seven chapters, we have examined various factors that influence the allelic frequencies in the gene pools of populations. It is easy to become mired in the details and lose sight of the broad, overall picture. The purpose of this chapter is to summarize how genetic variation is developed, maintained and directed in populations, a process which we call microevolution, so that we can develop an overview and general understanding of the interrelationships of the various factors or processes. The scheme that we will be following is summarized in Fig. 13.1. Evolution can be considered to be a two-step process: first, the production of genetic variation by mutations and genetic recombination and, second, an ordering of that variation by natural selection which may be influenced by processes such as genetic drift and migration. Mutations Genetic variation is originally created by mutations, which cause changes in the precise sequence of DNA in the chromosomes. Mutation by itself is not an important driving force of evolutionary change because mutations causing the same phenotypic change occur at very low frequencies, somewhere in the order of 1 × 10 -5 to 10 -8 per gamete. It would take many thousands of generations to effect a substantial change in allelic frequency as a result of mutation pressure alone (see Chapter 7). Mutation creates genetic variation in a non-directed fashion, i.e. mutations are not created in relation to their need.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".