Partnership between bees and flowers: Fine-tuned feeding speed and nectar sugar concentration facilitate energy rewards and pollination service
Bibliographic record
Abstract
Abstract The interaction between bee pollinators and flowering plants lays the foundation of biodiversity and food production for human livelihoods, with three out of four crops worldwide relying on this interaction. The ongoing success of this mutualism is pow-ered by the bilateral and potentially dueling demands of food procurement for polli-nators and sexual reproduction for plants. However, the underlying mechanisms of the mutualism, which must simultaneously fulfil the requirements from both sides, remain unexplored. Here we establish a biomechanical framework that combines la-boratory tests and mathematical models to investigate the relationship between bees and plants, using the feeding speed of bees and the nectar sugar concentration of flowers as the core factors. We find that both the optimal frequency of a bee dipping nectar and the nectar sugar concentration are delicately balanced over narrow ranges to simultaneously satisfy demands of both parties of the mutualism. Human activities and climate change can perturb this balanced interaction, which would be detri-mental to this critical pollination system essential to staple crops around the world.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".