Variability of Rates of Mutation and Fitness Decline During Mutation Accumulation in Escherichia coli Isolates
Bibliographic record
Abstract
During infectious disease outbreak investigations, mutation rates amongst lineages of clinical bacterial pathogens can be highly variable; what is classified as multiple outbreaks could indicate high genetic variation amongst descendants of a single outbreak event.Consequently, the best way to define the genetic boundaries of an outbreak cluster is currently unclear.Over 2720 generations of mutation accumulation on average, I explored mutation rate and fitness decline variation in nine clinical isolates of Escherichia coli and I found that there was high variation between, but less commonly within, genotypes.Genotypes could be generally be categorised by mutation rate and fitness decline variation between replicates as either: (1) non-mutator genotypes with low variation, non-mutator genotypes with high variation because of (2) (an) infrequent mutator replicate(s), or (3) mutator genotypes with high or low variation.My findings have important implications both for molecular epidemiology of bacterial pathogens and predicting evolution in pathogen pathways.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".