Bibliographic record
Abstract
This thesis investigates how breaking apart selection interference (‘Hill-Robertson’ effects) \nthat arises between linked loci can select for higher levels of recombination. \nSpecifically, it mainly studies how the presence of both advantageous and deleterious \nmutation affects selection for recombination. These evolutionary advantages are subsequently \ninvestigated with regards to sex resisting asexual invasion in a subdivided \npopulation. \ni) KEIGHTLEY and OTTO (2006) showed a strong advantage to recombination in \nbreaking apart selection interference, if it acts across multiple, linked loci subject to \nrecurrent deleterious mutation. Their model is modified to consider selection acting \non recombination if a small proportion of mutations are advantageous. This leads \nto a greater increase in selection acting on a recombination modifier, compared to \ncases where only deleterious mutations are present. \nii) Branching-process methods are developed to quantify how likely it is that a deleterious \nmutant hitchhikes with a selective sweep, and how recombination between \nthe two loci affects this process. This is compared to the neutral hitchhiking model, \nto determine how levels of linked neutral diversity would differ between the two \nscenarios. A simple application with regards to human genetic data is provided. \niii) Population subdivision can maintain costly sex, as a consequence of restricted gene \nflow slowing the spread of invading asexuals, which leads to an excessive accumulation \nof deleterious alleles. However, previous work did not quantify whether \ncostly sex can be maintained with realistic levels of population subdivision. Simulations \nin this thesis show that the level of population subdivision (as measured by \nFst) needed to maintain costly sex decreases with larger population size; however \ncritical Fst values found are generally high, compared to surveys of geographicallyclose \npopulations. The lowest levels of population subdivision that maintained sex \nwere found if mutation is both advantageous and deleterious, and demes were arranged \nin a one-dimensional stepping-stone formation. \niv) An analytical method is developed to calculate how long it takes an advantageous \nmutation (such as an invading asexual) to spread through a subdivided population. \nThe flexibility of the methods created means that they can be applied to different \ntypes of stepping-stone populations. It is shown how to formulate the fixation \ntime for one-dimensional and two-dimensional structures, with analytical methods \nshowing a good fit to simulation data.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".