Bibliographic record
Abstract
Understanding how adaptive evolution proceeds in populations experiencing different environments is the basis of much of evolutionary biology. Genetic variation is required for evolutionary change by natural selection to occur, and that genetic variation is oftentimes correlated among traits. Selection acts on the entirety of an organism’s phenotype and is thusalso best considered in a multivariate context. The interaction between selection and the organization of genetic variation together produces the evolutionary trajectory of a population. In my doctoral work, I have explored the influence of genetic correlations between traits on the response of populations to selection in populations across a geographic range, using the Ivyleaf Morning Glory (Ipomoea hederacea). I demonstrate the constancy of these genetic correlations among populations in the face of the destabilizing force of genetic drift. I investigate how genetic correlations and strong natural selection have shaped divergence among populations across space. Additionally, I consider what these evolutionary patterns suggest about the maintenance of range limits and potential for future northward expansion of Ipomoea hederacea. I find overall stability of the genetic relationships among quantitative traits, and strong selection which acts counter to the majority of the genetic variation. The observed divergence among populations has occurred despite the constraining influence of their quantitative genetic architecture. Future adaptive evolution beyond the range margin will largely depend on the source population, as minor difference in the structure and quantity of genetic variation may have critical impact of population persistence. Further, I demonstrate the potential for using machine learning to predict fitness using gene expression data from a field experiment with I. hederacea. I find that photosynthesis-related genes, and genes related to stress response are major contributors to fitness differences.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".