Contrasting pollination efficiency and effectiveness among flower visitors of <i>Malva sylvestris, Borago officinalis</i> and <i>Onobrychis viciifolia</i>
Bibliographic record
Abstract
Biotic pollination is an important factor for ecosystem functioning and provides a substantial ecosystem service to human food security. Not all flower visitors are pollinators, however, and pollinators differ in their pollination performances. In this study, we determined the efficiencies of flower visitors to the plant species Malva sylvestris, Borago officinalis and Onobrychis viciifolia by analysing stigmatic pollen deposition. We further calculated pollinator effectiveness by scaling up single-visit pollen deposition using visitation frequency. Flower-visitor groups differed in their efficiencies at the single-visit level and not all of them deposited more pollen compared to unvisited stigmas. Bumblebees tended to be most efficient in depositing pollen per single visit across the three plant species. Due to the by far highest visitation frequencies, Apis mellifera showed the highest effectiveness in depositing pollen per hour for M. sylvestris and B. officinalis, but not for O. viciifolia, for which the Bombus lapidarius complex was both the most frequent and the most effective pollinator group. Hence, the most frequent flower visitors were most effective in our study. For non-dominant pollinator groups, however, visitation frequencies contributed disproportionally to pollinator effectiveness. Thus, combining pollen deposition per single-visit with visitation frequency is necessary to reveal true pollinator performance and to better understand flower-visitor interactions.
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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 | 0.000 |
| 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) | 0.001 | 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".