Artificial Selection and Domestication: Modern Lessons from Darwin’s Enduring Analogy
Bibliographic record
Abstract
It is clear from his published works that Charles Darwin considered domestication to be very useful in exploring and explaining mechanisms of evolutionary change. Not only did domestication occupy the introductory chapter of On the Origin of Species , but he revisited the topic in a two-volume treatise less than a decade later. In addition to drawing much of his information about heredity from studies of domesticated animals and plants, Darwin saw important parallels between the process of artificial selection by humans and natural selection by the environment. There was resistance to this analogy even among Darwin’s contemporary supporters when it was proposed, and there also has been disagreement among historians and philosophers regarding the role that the analogy with artificial selection actually played in the discovery of natural selection. Regardless of these issues, the analogy between artificial and natural selection remains important in both research and education in evolution. In particular, the present article reviews ten lessons about evolution that can be drawn from the modern understanding of domestication and artificial selection. In the process, a basic overview is provided of current approaches and knowledge in this rapidly advancing field.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".