SEXUAL, FECUNDITY, AND VIABILITY SELECTION ON FLOWER SIZE AND NUMBER IN A SEXUALLY DIMORPHIC PLANT
Bibliographic record
Abstract
The evolution of sexual dimorphism will depend on how sexual, fecundity and viability selection act within each sex, with the different forms of selection potentially operating in opposing directions. We examined selection in the dioecious plant Silene latifolia using planted arrays of selection lines that differed in flower size (small vs. large). In this species, a flower size/number trade-off exists within each sex, and males produce smaller and more numerous flowers than females. Moreover, floral traits are genetically correlated with leaf physiology. Sexual selection favoring males in the small-flower line occurred via greater overlap in the timing of flower output between males from this line and females. Fecundity selection favored males with high flower production, as siring success was proportionate to pollen production. Viability selection opposed sexual selection, favoring males from the large-flower line. In females, fecundity and viability selection operated in the same direction, favoring those from the large-flower line via greater seed production and survival. These results concur with the pattern of floral sexual dimorphism. Together with previous results they suggest that the outcome of the different forms of selection will be environmentally dependent, and therefore help to explain variation among populations in sexually dimorphic traits.
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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 | 0.001 |
| 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) | 0.001 | 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".