Modularity and selection of nectar traits in the evolution of the selfing syndrome in <i>Ipomoea lacunosa</i> (Convolvulaceae)
Bibliographic record
Abstract
SUMMARY Although the evolution of the selfing syndrome often involves reductions in floral size, pollen, and nectar, few studies of selfing syndrome divergence have examined nectar. We investigate whether nectar traits have evolved independently of other floral size traits in the selfing syndrome, whether nectar traits diverged due to drift or selection, and the extent to which quantitative trait locus (QTL) analyses predict genetic correlations. We use F5 recombinant inbred lines (RILs) generated from a cross between Ipomoea cordatotriloba and I. lacunosa . We calculate genetic correlations to identify evolutionary modules, test whether traits have been under selection, identify QTLs, and perform correlation analyses to evaluate how well QTL properties reflect the genetic correlations. Nectar and floral size traits form separate genetic clusters. Directional selection has acted to reduce nectar traits in the selfing I. lacunosa . Calculations from QTL properties are consistent with observed genetic correlations. Floral trait divergence during mating system syndrome evolution reflects independent evolution of at least two evolutionary modules: nectar and floral size traits. This independence implies that adaptive change in these modules requires direct selection on both floral size and nectar traits. Our study also supports the expected mechanistic link between QTL properties and genetic correlations.
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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".