Bibliographic record
Abstract
Google Scholar Google Preview WorldCat Elements of Evolutionary Genetics is the new book by Brian and Deborah Charlesworth. Over the last three plus decades, the Charlesworths have worked on problems theoretical and empirical, advancing our understanding of phenotypic and molecular evolution in both plant and animal systems. The number and quality of their contributions is staggering. It would be difficult to imagine two more qualified individuals to write such a book. Their text covers a wide range of topics from classic (phenotypic) issues such as mutation load and mating system evolution to more modern (molecular) problems such as coalescence in subdivided populations and inferences from allele frequency spectra. As with their research, their book is deeply rooted in theory. A substantial fraction of every chapter consists of key theoretical results, typically along with the derivation itself (or a skeleton version thereof), and some discussion of the model's assumptions and implications. Empirical data are discussed at length in a few sections but, in most cases, examples are only briefly mentioned or simply cited. The emphasis of the book is on exposing the reader to the relevant theory rather than providing a comprehensive review of empirical results.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".