The tripping mechanism of flowers affects pollen transfer dynamics
Bibliographic record
Abstract
Insect pollinators affect pollen transfer dynamics, with consequences for pollen movement and the genetic structure of plant populations. Pollen transfer dynamics has not been previously examined in flowers with a tripping mechanism. Here we examine whether pollen accumulated on a bee’s body increases with the number of Medicago sativa L. flowers tripped by Bombus impatiens Cresson during a foraging bout, while controlling for bee body size and number of visited flowers. In a second experiment, we determine whether the number of revisits to a tripped flower increases pollen deposition onto the stigmas. We set up three M. sativa plants with a controlled number of racemes in a greenhouse room, and followed individual bees as they foraged, recording each plant, raceme, and flower visited. For pollen accumulated, we collected bees at the end of their foraging bout and counted pollen grains on their body. For pollen deposition, we collected flowers with between 0 and 6 revisits and counted the pollen grains on the stigmas. The number of pollen grains on a bee’s body increased with the number of flowers tripped in a foraging bout, but was not affected by the number of flowers visited or the size of individual bees. The number of pollen grains deposited on a stigma did not increase with the number of revisits to a tripped flower. This latter result contrasts with plants without a tripping mechanism where the number of visits increases pollen deposition and seed set. Tripping affects pollen transfer dynamics and we discuss how its effect may vary with the mode of tripping.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".