Adaptive plasticity of floral display size in animal-pollinated plants
Bibliographic record
Abstract
Plants need not participate passively in their own mating, despite their immobility and reliance on pollen vectors. Instead, plants may respond to their recent pollination experience by adjusting the number of flowers that they display simultaneously. Such responsiveness could arise from the dependence of floral display size on the longevity of individual flowers, which varies with pollination rate in many plant species. By hand-pollinating some inflorescences, but not others, we demonstrate plasticity in display size of the orchid Satyrium longicauda. Pollination induced flower wilting, but did not affect the opening of new flowers, so that within a few days pollinated inflorescences displayed fewer flowers than unpollinated inflorescences. During subsequent exposure to intensive natural pollination, pollen removal and receipt increased proportionally with increasing display size, whereas pollen-removal failure and self-pollination accelerated. Such benefit-cost relations allow plants that adjust display size in response to the prevailing pollination rate to increase their attractiveness when pollinators are rare (large displays), or to limit mating costs when pollinators are abundant (small displays). Seen from this perspective, pollination-induced flower wilting serves the entire plant by allowing it to display the number of flowers that is appropriate for the current pollination environment.
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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 | 0.001 |
| 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".