Color, size, shape: The drivers of floral variation in Hesperis matronalis (Dames Rocket)
Bibliographic record
Abstract
Evolutionary biologists have long been intrigued by the factors that sustain genetic and phenotypic variation within and among natural populations. Polymorphisms underlying components of floral display -- such as floral color, size, and shape -- are uniquely of interest since variation in these traits impact pollinator attraction, rates of visitation, and pollinator efficiency, which ultimately influence patterns of plant reproduction and therefore fitness. We leverage existing floral variation present within and among populations of Hesperis matronalis (Dames Rocket) to disentangle the relative influence of natural selection and genetic drift in shaping floral trait variation. We employ a multi-tiered approach: we determine if variation in floral traits (color, size, and petal shape) is influenced by geography or environmental variables such as temperature and precipitation, we evaluate whether selection underlies trait variation by comparing phenotypic divergence (PST) with neutral genetic structure (FST), and we perform a Lande-Arnold selection analysis to explore the relationship between fitness and floral trait variation within natural populations. We find that selection underlies the divergence of floral color, floral size, and petal width among H. matronalis populations, with PST > FST for each trait. We find no indication, however, that variation in size in this species is influenced by the environment, but some evidence that variation in floral color and petal shape may be influenced by temperature. Finally, selection analyses of contemporary populations indicate divergent selection affecting combinations of color, petal shape, and plant size. These results suggest that the variation in floral shape in this species may be maintained due to environmental pressures, whereas floral color is influenced by pollinator visibility and the presence of different pollinator groups.
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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 | 0.000 |
| 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) | 0.001 | 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".