Bibliographic record
Abstract
The traditional view is that evolution proceeds very slowly, over immense periods of time, driven by weak selection acting on innumerable genes of small effect. Recent studies of rapid evolution, in the laboratory and in the field, have given a radically different picture. Although beneficial mutations tend to be small in effect when they first appear, those that survive to spread and become fixed are usually among the minority with large effect. Hence, although hundreds of loci of small effect may contribute to variation in character state, adaptation is predominantly caused by alleles of large effect. This leads to the hope that the particular mutations responsible for adaptation to altered conditions of life can be identified and characterized. This has been achieved in some cases and may soon become routine. Furthermore, it raises the possibility that adaptive change can be predicted from a knowledge of genetics and ecology. Experimental evolution suggests that any given selection line that is adapting to changed conditions will follow one of a few themes (broadly speaking, loci), each of which may have many variations (mutations within the locus producing similar phenotypes). Hence, evolutionary change can be predicted only within limits, even in principle. Nevertheless, recent attempts to predict how very simple genomes change have been surprisingly successful, and we may be close to a new predictive understanding of the genetic basis of adaptation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".