Comparative analysis of pollen release biomechanics in <i>Thalictrum</i>: implications for evolutionary transitions between animal and wind pollination
Bibliographic record
Abstract
Summary Transitions from animal to wind pollination have occurred repeatedly in flowering plants, driven by structural and biomechanical modifications to flowers. But the initial changes promoting wind pollination are poorly understood, especially those required to release pollen into airflows – the critical first stage of wind pollination. Using a wind tunnel, we performed a comparative study of pollen release biomechanics in 36 species of animal‐ and wind‐pollinated Thalictrum. We quantified pollination syndromes and stamen natural frequency (fn), a key vibration parameter, to determine if floral traits reliably predicted pollen release probability. We then investigated if pollen release was caused by wind‐induced resonance vibration of stamens. We detected wind‐induced stamen resonance in 91% of species and a strong effect of stamen acceleration on pollen release, inversely driven by fn. However, unlike fn, pollination syndromes did not reliably predict the probability of pollen release among species. Our results directly link fn to the capacity of stamens to release pollen by wind and suggest that structural mechanisms reducing fn are likely to be important for initiating transitions from animal to wind pollination. Our inability to predict the probability of pollen release based on pollination syndromes suggests diverse phenotypic trajectories from animal 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 | 0.000 |
| 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".