The economy of pollen dispersal in flowering plants
Bibliographic record
Abstract
Mating success of flowering plants depends strongly on the efficiencies of pollen removal from flowers and its subsequent dispersal to conspecific stigmas. We characterized the economy of pollen dispersal in flowering plants by analysing pollen fates and their correlates for 228 species. The mean percentage of pollen removed from flowers (removal efficiency) varied almost twofold according to the type of pollen-dispersal unit, from less than 45% for orchids and milkweeds with solid pollinia, to greater than 80% for species with granular monads or sectile (segmented) pollinia. The mean percentage of removed pollen reaching stigmas (pollen transfer efficiency, PTE) varied from 2.4% for species with separate monads to 27.0% for orchids with solid pollinia. These values tended to be higher in plants with single pollinator species and in those with non-grooming pollinators. Nectar production increased removal efficiency, but did not influence PTE. Among types of pollen-dispersal units, the net percentage of produced pollen that was dispersed to stigmas varied negatively with removal efficiency and positively with PTE, indicating the relative importance of the latter for overall pollen economy. These findings confirm the key importance of floral traits, particularly pollen packaging, for pollen dispersal outcomes and highlight the under-appreciated pollination efficiency of non-grooming pollinators.
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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 | 0.001 | 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".