Consequences of Multiple Inflorescences and Clonality for Pollinator Behavior and Plant Mating
Bibliographic record
Abstract
Angiosperms engage in distributed reproduction, producing sex organs in multiple flowers on one or more inflorescences, including on different physical individuals of clonal plants. We investigated the effects of alternative deployments of artificial flowers for pollinator behavior and simulated pollen dispersal. Plants presented 18 flowers on either one inflorescence (1-I plants) or three inflorescences (3-I plants) spaced either closely or widely. Bees often skipped inflorescences on 3-I plants, visiting an average of 1.5 fewer flowers overall than on 1-I plants. In simulations with all flowers receiving and donating pollen, this behavior caused 7% less geitonogamy for 3-I plants, contradicting a common supposition that clonality increases geitonogamy. Bees generally moved upward within inflorescences and downward between inflorescences. Consequently, in simulations, segregation of pollen receipt to lower flowers and pollen donation to upper flowers reduced self-pollination and enhanced pollen export much more for 1-I plants. Nectar volume per flower had little relevant influence on bee behavior. The observed bee responses and simulated mating results suggest that production of multiple inflorescences and clonality promote pollination quality when flowers simultaneously receive and donate pollen, whereas a single large inflorescence is advantageous when segregation of sex roles among flowers reduces geitonogamy effectively.
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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 | 0.002 |
| 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 | 0.001 | 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".