Inflorescence architecture and wind pollination in six grass species
Bibliographic record
Abstract
Summary Inflorescence architecture and floral morphology vary extensively within the Poaceae, but the functional significance of this variation remains largely unknown. As grasses are wind‐pollinated, their inflorescence diversity probably reflects alternate solutions to manipulating airstreams to enhance pollen export and import. We tested this hypothesis with two field experiments that contrasted pollen removal and receipt by compact and diffuse inflorescences. In the ‘aggregation’ experiment, we tied together panicle branches of two species with diffuse inflorescences, creating more compact inflorescences. Aggregation reduced pollen removal from both species, probably by increasing boundary‐layer thickness. The effects of inflorescence aggregation differed between the two species in a manner that is consistent with pollen‐size differences, which could affect the ability of pollen grains to pass through the thickened boundary layer around stigmas. The ‘staking’ experiment constrained inflorescence motion and revealed that culm characteristics contribute to the interaction between grass inflorescences and airstreams. In particular, inflorescence oscillation principally serves pollen removal for species with compact inflorescences, but is of primary importance in pollen receipt for species with diffuse architectures. These results suggest that inflorescence architecture interacts with wind in a complex manner to facilitate pollination and supports the hypothesis that the extensive diversity of inflorescence architecture within the Poaceae represents evolutionary solutions to the physical constraints of 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) | 0.000 | 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".