The Evolutionary Genetics of Gene Expression in Capsella grandiflora
Bibliographic record
Abstract
Understanding the evolutionary forces that maintain variation at the sequence and phenotypic level is crucial for developing a full understanding of how evolution works in nature. In my thesis, I investigate the evolutionary forces that maintain genetic variation at the sequence level and at the phenotype level using genomic and transcriptomic data from the plant Capsella grandiflora. In Chapter 2, I show that negative selection and positive selection shape sequence variation in coding and conserved noncoding sequence and that a gene's expression level influences the strength of negative selection. In Chapter 3, I map the loci that affect expression level genome-wide and show that negative selection is the dominant force acting on these loci, consistent with the hypothesis of mutation-selection balance. In Chapter 4, I show that genes with higher coexpression network connectivity experience stronger positive and negative selection, suggesting that pleiotropy, as measured by network connectivity, increases constraint but does not limit adaptation. Overall, my results suggest that mutation-selection balance explains much of the genetic variation observed within populations at both the sequence and phenotypic level.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".