Divergent selection on the biomechanical properties of stamens under wind and insect pollination
Bibliographic record
Abstract
Wind pollination has evolved from insect pollination in numerous angiosperm lineages and is associated with a characteristic syndrome of morphological traits. The traits initiating transitions to wind pollination and the ecological drivers involved are poorly understood. Here, we examine this problem in Thalictrum pubescens , an ambophilous (insect and wind pollination) species that probably represents a transitional state in the evolution of wind pollination. We investigated wind-induced pollen release by forced harmonic motion by measuring stamen natural frequency ( f n ), a key vibration parameter, and its variability among nine populations. We assessed the repeatability of f n over consecutive growing seasons, the effect of this parameter on pollen release in a wind tunnel, and male reproductive success in the field using experimental manipulation of the presence or absence of pollinators. We found significant differences among populations and high repeatability within genotypes in f n . The wind tunnel assay revealed a strong negative correlation between f n and pollen release. Siring success was greatest for plants with lower f n when pollinators were absent, but this advantage diminished when pollinators were present. Our biomechanical analysis of the wind–flower interface has identified f n as a key trait for understanding early stages in the transition from insect to wind pollination.
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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 | 0.001 | 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".