Genotype and environment effects on sunflower nectar and their relationships to crop pollination
Bibliographic record
Abstract
Whether caused by genotype (G) or environment (E), floral trait variation has consequences for plants and their pollinators. Cultivated sunflower is a model system to explore floral trait variation; though sunflowers are bred to self-pollinate, benefits of pollination by bees remain substantial. To better understand sunflower-pollinator interactions, experiments were conducted to: (i) examine genotype and environment effects on nectar quantity and quality under controlled conditions, and (ii) assess effects of bags used for pollinator exclusion on nectar quantity, quality and bee foraging in a field environment. Contrasting temperature treatments (28°C, 21°C, 28°C / 16°C) reveal environment effects or G × E interactions for nectar volume (µl / floret), concentration (°Brix), and sugar composition (% sucrose). Bags used to exclude sunflower pollinators resulted in nectar volumes greater than plants with unrestricted access for bees (= open-pollination), and in ≈ 5-fold increased visitation by wild bees after bags were removed. Differences in bee visits to plants that were previously bagged versus plants never bagged decreased over the 2 h following bag removal. Though genetic variation in sunflower nectar is affected by the environment and G × E interactions, improving pollination via plant breeding still appears feasible. Future research on intraspecific variation in pollen rewards could be helpful, especially because pollen has received little research compared to nectar. For research with nectar or pollen, it seems desirable to measure floral rewards with methods that don’t rely on pollinator exclusion (bags or cages), which should provide more realistic data on what pollinators experience while foraging.
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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".