Evolvability and Pleiotropic Constraint
Bibliographic record
Abstract
Evolvability is a population's ability to change its underlying genetic makeup through natural selection, to evolve. Most measures of short-term evolvability relate genetic change to the abundance and geometry of standing genetic variation, making the additional assumption that this structure is captured by the genetic variance-covariance matrix G; G applies in only the multivariate normal scenario. Because we observe non-normality in natural distributions and are often unable to verify statistically whether a particular distribution is normal, we propose an alternate approach to discussing the short-term evolvability of general populations. Changes in the underlying genetic makeup of a population are observed as changes in the relative frequency of the population's traits. This is fundamentally a change in distribution, and is best quantified using an information-theoretical approach. The resulting measure of evolvability and its constraints apply to any well-behaved distribution and are for normally distributed traits a function of G; we suggest that existing measures may be placed within our general formulation. Comparing the total constraint to the sum of univariate constraints on individual traits we quantify the constraint due to multivariate trait interactions; we propose this as an appropriate measure of pleiotropic constraint. We find that pleiotropic constraint is highly dependent on the total correlation information, and to advance towards a suitable null hypothesis for tests of pleiotropy we derive the distribution of this quantity under multivariate normal independence. For large system sizes this relates to the Marchenko-Pasteur distribution, and is approximately normal, obeying Lyapunov's central limit theorem. We demonstrate the unique theoretical and practical advantages of a distribution-level approach to evolvability using both simulated and real data from Drosophila. In line with intuition, incorrectly assuming an absence of non-normality in the distribution of phenotypic traits leads to underestimation of the evolutionary constraint.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".