Functional associations of floret and inflorescence traits among grass species
Bibliographic record
Abstract
The aerodynamics of wind pollination selects for an intimate relation between form and function in anemophilous plants. Inflorescence architecture and floral morphology vary extensively within the Poaceae, but the functional implication of this variation remains largely unknown. Here we quantify associations between floret, culm, and inflorescence characteristics for 25 grass species in Kananaskis, Alberta, Canada, and consider whether different architectures may implement unique mechanisms to aid pollination. The species cluster into four categories defined by all combinations of floret size (small vs. large) and inflorescence architecture (compact vs. diffuse). Species differed significantly for all 18 traits that we measured, with 12 traits differing only between floret-size classes, three differing only between inflorescence types, and three differing among both (independently or by an interaction). Based on these morphological associations, we discuss the aerodynamic and functional consequences of each category for wind pollination. The independence of inflorescence and floral traits has probably allowed exploration of all possible combinations of inflorescence architecture and floret size during the evolution of the Poaceae.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".