Evolutionary insight from whole‐genome sequencing of experimentally evolved microbes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Experimental evolution (EE) combined with whole-genome sequencing (WGS) has become a compelling approach to study the fundamental mechanisms and processes that drive evolution. Most EE-WGS studies published to date have used microbes, owing to their ease of propagation and manipulation in the laboratory and relatively small genome sizes. These experiments are particularly suited to answer long-standing questions such as: How many mutations underlie adaptive evolution, and how are they distributed across the genome and through time? Are there general rules or principles governing which genes contribute to adaptation, and are certain kinds of genes more likely to be targets than others? How common is epistasis among adaptive mutations, and what does this reveal about the variety of genetic routes to adaptation? How common is parallel evolution, where the same mutations evolve repeatedly and independently in response to similar selective pressures? Here, we summarize the significant findings of this body of work, identify important emerging trends and propose promising directions for future research. We also outline an example of a computational pipeline for use in EE-WGS studies, based on freely available bioinformatics tools.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it