The SHAPE of logistic growth shows that serial passaging biases fixation probability
Bibliographic record
Abstract
Abstract The forward time simulation tool rSHAPE (R-package for Simulated Haploid Asexual Population Evolution) was designed to complement the theoretical and empirical study of evolution. Included with rSHAPE are functions to programmatically build, run, and initially process results of an evolutionary experiment defined by the range of experimental conditions. As experimental evolution often studies both the emergence and fate of de novo mutants, I validated rSHAPE by confirming its ability to replicate seminal theoretical expectations concerning changes in fitness through time and the fixation probability of mutants. As an example of how rSHAPE can support both theoretical and empirical research, I applied rSHAPE to study how the laboratory protocol of serial passaging, common in microbial experimental evolution, affects the fixation probability of de novo mutants. Unlike related theoretical work which modelled growth as effectively exponential (Wahl et al. , 2002), this study considered populations experiencing logistic growth which is common to microbes undergoing serial passaging. In contrast to exponential growth, when a population undergoes logistic growth the probability of a mutant arising and eventually fixing depends upon when a mutant arises during a growth phase. Users can download software and documentation for rSHAPE through CRAN at https://cran.r-project.org/web/packages/rSHAPE/index.html , or via GitHub at https://www.github.com/Jdench/SHAPE_library/ .
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".