Inflorescence characteristics as function‐valued traits: Analysis of heritability and selection on architectural effects
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Bibliographic record
Abstract
Abstract Production of multiple flowers in inflorescences allows the reproductive phenotypes of individual plants to include systematic among‐flower variation, which could be adaptive. Systematic trait variation within inflorescences could arise from resource competition among flowers, or be a developmentally determined feature of flower position, regardless of resource dynamics. The latter, architectural effect typically manifests as continuous floral variation within inflorescences. For architectural effects to be adaptive, floral trait variation among individuals must covary with reproductive performance and be heritable. However, heritability and phenotypic selection on gradients of variation cannot be estimated readily with traditional statistical approaches. Instead, we advocate and illustrate the application of two functional data analysis techniques with observations of Delphinium glaucum (Ranunculaceae). To demonstrate the parameters‐as‐data approach we quantify heritability of variation in anthesis rate, as represented by the regression coefficient relating daily anthesis rate to inflorescence age. SNP‐based estimates detected significant heritability ( h 2 = 0.245) for declining anthesis rate within inflorescences. Functional regression was used to assess phenotypic selection on anthesis rate and a floral trait (lower sepal length). The approach used spline curves that characterize within‐inflorescence variation as functional predictors of a plant's fruit set. Selection on anthesis rate varied with inflorescence age and the duration of an individual's anthesis period. Lower sepal length experienced positive selection for basal and distal flowers, but negative selection for central flowers. These results illustrate the utility and power of functional‐data analyses for studying architectural effects and specifically demonstrate that these effects are subject to natural selection and hence adaptive.
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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 | 0.000 | 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 it