THE SCENT OF A MALE: THE ROLE OF FLORAL VOLATILES IN POLLINATION OF A GENDER DIMORPHIC PLANT
Bibliographic record
Abstract
Most flowering plants rely on animal pollinators to transfer male gametes between individuals, and thus a significant problem for gender dimorphic plants is that pollinators often avoid female flowers. Here we show for the first time that one important reason pollinators shun female flowers is because they do not smell like males. We compared emission rates and floral scent composition in a gynodioecious wild strawberry (Fragaria virginiana) where females receive half as many visits by generalist pollinators as conspecific hermaphrodites. We used floral extracts to determine the source of sexually dimorphic odor and pollinator responses. Specifically, we used extracts of whole flowers and specific floral parts in choice tests to determine that pollinators preferred the scent of hermaphrodite flowers over those of females and that this discrimination was due primarily to the scent of hermaphrodite anthers. These data conclusively show that scent can be a major driver of pollinator behavior in gender dimorphic plants. Our results also indicate that scent is an important modulator of pollinator behavior even in a small flowered, weakly scented species visited by generalist pollinators, and not just peculiar to intensely scented, deceptive, or specialized pollination systems.
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 | 0.000 |
| 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 | 0.000 | 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".