Bioprospecting for improved floral fragrance in wild sunflowers
Bibliographic record
Abstract
Abstract Cultivated sunflower ( Helianthus annuus ) is not typically considered to have a pleasant floral fragrance. In field production, seed yield is often limited by pollination services, particularly in the production of hybrid seed. Improved floral fragrance, as determined by volatile organic compounds, may be a route to improving pollinator attraction for oilseed and confectionary production and could also add value to ornamental sunflowers. Wild relatives of H . annuus have a long history of being used to breed improved traits into cultivated varieties, yet it is unknown whether favorable scents are present in wild Helianthus species and thus an available resource for fragrance breeding. In order to assess the diversity of floral fragrance available in crop wild relatives, 30 diverse accessions of wild Helianthus as well as seven varieties of H. annuus spanning a domestication gradient were grown in greenhouse experiments and variation in floral volatiles was analyzed by solid phase microextraction-gas chromatography-mass spectrometry. While alpha-pinene made up a significant portion of the volatiles emitted for most taxa, there was substantial diversity present across the genus as well as within H. annuus . Most volatiles emitted were monoterpenoids with a significant share of sesquiterpenoids. The diversity identified here will inform further targeted study of which compounds affect pollinator attraction and health. Several wild accessions such as Helianthus debilis subsp. tardiflorus and Helianthus praecox subsp. praecox as well as open-pollinated domesticated accessions of H. annuus show promise for breeding for improved floral fragrance due to high volatile abundance and likely favorable compound compositions.
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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".