Ovule number per flower in a world of unpredictable pollination
Bibliographic record
Abstract
The number of ovules per flower varies over several orders of magnitude among angiosperms. Here we consider evidence that stochastic uncertainty in pollen receipt and ovule fertilization has been a selective factor in the evolution of ovule number per flower. We hypothesize that stochastic variation in floral mating success creates an advantage to producing many ovules per flower because a plant will often gain more fitness from occasional abundant seed production in randomly successful flowers than it loses in resource commitment to less successful flowers. Greater statistical dispersion in pollination and fertilization among flowers increases the frequency of windfall success, which should increase the strength of selection for greater ovule number per flower. We therefore looked for evidence of a positive relationship between ovule number per flower and the statistical dispersion of pollen receipt or seed number per flower in a comparative analysis involving 187 angiosperm species. We found strong evidence of such a relationship. Our results support the hypothesis that unpredictable variation in mating success at the floral level has been a factor in the evolution of ovule packaging in angiosperms.
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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 | 0.002 |
| 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 | 0.001 |
| 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".