A Unified Approach to the Evolutionary Consequences of Genetic and Nongenetic Inheritance
Bibliographic record
Abstract
Inheritance-the influence of ancestors on the phenotypes of their descendants-translates natural selection into evolutionary change. For the past century, inheritance has been conceptualized almost exclusively as the transmission of DNA sequence variation from parents to offspring in accordance with Mendelian rules, but advances in cell and developmental biology have now revealed a rich array of inheritance mechanisms. This empirical evidence calls for a unified conception of inheritance that combines genetic and nongenetic mechanisms and encompasses the known range of transgenerational effects, including the transmission of genetic and epigenetic variation, the transmission of plastic phenotypes (acquired traits), and the effects of parental environment and genotype on offspring phenotype. We propose a unified theoretical framework based on the Price equation that can be used to model evolution under an expanded inheritance concept that combines the effects of genetic and nongenetic inheritance. To illustrate the utility and generality of this framework, we show how it can be applied to a variety of scenarios, including nontransmissible environmental noise, maternal effects, indirect genetic effects, transgenerational epigenetic inheritance, RNA-mediated inheritance, and cultural inheritance.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".