Flower orientation influences the consistency of bumblebee movement within inflorescences
Bibliographic record
Abstract
BACKGROUND AND AIMS: Plant species differ greatly in the three-dimensional arrangements of their flowers (inflorescence architecture). However, the nature of selection responsible for this diversity is poorly understood. Studies that examine among-species variation suggest that inflorescence architecture affects pollinator behaviour, and so should influence plant mating. However, few studies consider the consequences of within-population architectural variation for pollinator behaviour. METHODS: We manipulated inflorescence architecture of Delphinium glaucum to contrast bumblebee responses to normal and one-sided (secund) inflorescences. KEY RESULTS: The 'dimensionality' of manipulated inflorescences did not affect the number of flowers that bees visited; however, bees moved upward proportionally more on secund inflorescences. CONCLUSIONS: This study shows that realistic within-population variation in inflorescence architecture can manipulate pollinator behaviour. These results bear important consequences for plant mating success and the coordinated evolution of inflorescence architecture and floral specialization within inflorescences. These results also question why secund inflorescences are rare, for which we propose four testable explanations.
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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 | 0.001 | 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".