Bibliographic record
Abstract
The history of life has been punctuated by unusually spectacular periods of evolutionary diversification called adaptive radiation. Darwin's finches in the Galapagos, cichlid fishes in African Rift and Nicaraguan crater lakes, and the emergence of mammals at the end of the Cretaceous are hallmark examples. Although we have learned much from these and other case studies about the mechanisms thought to drive adaptive radiations, convincing experimental tests of theory are often lacking for the simple reason that it is usually impossible to "rewind the tape of life," as Stephen Jay Gould was fond of saying, and run it again. This situation has changed dramatically in recent years with the increasing emphasis on the use of microbial populations which, because of their small size and rapid generation times, make possible the construction of replicated, manipulative experiments to study evolution in the laboratory. Here I review the contributions that microbial experimental evolution has made to our understanding of the ecological and genetic mechanisms underlying adaptive radiation. I focus on three major gaps in the theory of adaptive radiation--the paucity of direct tests of mechanism, the genetics of diversification, and the limits and constraints on the progress of radiations--with the aim of pointing the way toward the development of a more general theory of adaptive radiation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".