Direct and indirect responses to selection on pollen size in <i>Brassica rapa</i> L.
Bibliographic record
Abstract
Abstract Pollen size varies little within angiosperm species, but differs extensively between species, suggesting the action of strong selection. Nevertheless, the potential for genetic responses of pollen size to selection, as determined by additive genetic variance and genetic correlations with other floral traits, has received little attention. To assess this potential, we subjected Brassica rapa to artificial selection for large and small pollen during three generations. This selection caused significant divergence in pollen diameter, with additive genetic effects accounting for over 30% of the observed phenotypic variation in pollen size. Such heritable genetic variation suggests that natural selection could effect evolutionary change in this trait. Selection on pollen size also elicited correlated responses in pollen number (–), flower size (+), style length (+), and ovule number (+), suggesting that pollen size cannot evolve independently. The correlated responses of pollen number, flower size and ovule number probably reflect the genetically determined and physically constrained pattern of resource allocation in B. rapa. In contrast, the positive correlation between pollen size and style length may represent a widespread gametic-phase disequilibrium in angiosperms that arises from nonrandom fertilization success of large pollen in pistils with long styles.
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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) | 0.002 | 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".