Estimating pollinator performance of visitors to the self-incompatible crop-plant <i>Brassica rapa</i> by single visit deposition and pollen germination: a comparison of methods.
Bibliographic record
Abstract
Estimating the pollen-deposition effectiveness of flower visitors is fundamental to understanding their performance as pollinators. While estimates of visitation rates, pollen loads, and single visit deposition (SVD) are all useful proxies for performance, and so help to reveal the relative effectiveness of different visitors, none take into account the breeding system of the plants, or the quality of pollen deposited. Here we compare the performance of visitors to the self-incompatible plant Brassica rapa (turnip) using SVD and pollen germination. We also report the first use of the staining of Brassica rapa stigma papilla cells (known to reveal a specific reaction to self-pollen) to compare self-pollen deposition between insect visitors. We found that most of the pollen grains deposited by insect visitors (and therefore counted by SVD methods) were non-germinating self-pollen. A smaller proportion of grains were outcrossed and so germinated. There was also a significant positive relationship between environmental conditions (wind speed) and pollen deposition, but not pollen germination.Both methods identified Bombus spp. as the best-performing visitors on turnip flowers, followed by Eristalis spp., whereas performance estimates for Episyrphus balteatus and ‘other hoverflies’ were no higher than controls for both methods. This study provides further insight into the methodology for estimating pollinator performance, especially in plants when only cross-pollen can germinate.
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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 | 0.001 | 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 | 0.001 | 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".