Information entropy as a measure of genetic diversity and evolvability in colonization
Bibliographic record
Abstract
In recent years, several studies have examined the relationship between genetic diversity and establishment success in colonizing species. Many of these studies have shown that genetic diversity enhances establishment success. There are several hypotheses that might explain this pattern, and here I focus on the possibility that greater genetic diversity results in greater evolvability during colonization. Evaluating the importance of this mechanism first requires that we quantify evolvability. Currently, most measures of evolvability have been developed for quantitative traits whereas many studies of colonization success deal with discrete molecular markers or phenotypes. The purpose of this study is to derive a suitable measure of evolvability for such discrete data. I show that under certain assumptions, Shannon's information entropy of the allelic distribution provides a natural measure of evolvability. This helps to alleviate previous concerns about the interpretation of information entropy for genetic data. I also suggest that information entropy provides a natural generalization to previous measures of evolvability for quantitative traits when the trait distributions are not necessarily multivariate normal.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".