Progresses of the international community to understand sunflower–pollinator interactions through multiscale approaches
Bibliographic record
Abstract
The first web conference on sunflower–pollinator interactions gathered the international community around a major topic for sunflower productivity and ecosystemic biodiversity. Insect-mediated pollination is important for increasing sunflower seed yield, but is dependent on environmental factors. Climate change can impact sunflower-pollinator interactions by influencing both plant and pollinator behavior. Natural or artificially elevated ambient temperatures appear to accelerate floret development and advance the timing of pollen presentation to coincide with pollinator activities. Drought showed no major effect on morphological traits such anther or corolla length, but dramatically reduced nectar and pollen productions. Flavonol-glycoside conjugates lower head transpiration thereby mitigating deleterious effects of abiotic stresses. Wild pollinators show fine scale preferences among sunflower varieties, likely due to the different resource focus of wild and managed honeybees. Agricultural practices such as planting flower strips to provide nesting sites for wild pollinators, or supplementing crop plots with hives, can have a positive effect on insect-mediated pollination efficiency and ensure optimal yields. All together, recent results on sunflower–pollinator interactions pave the way to develop varieties and corresponding cropping systems more favorable to pollinator biodiversity while maintaining high yields in the context of climate change.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".