The bouquet of grapevine ( <i>Vitis vinifera</i> L. cv. Cabernet Sauvignon) flowers arises from the biosynthesis of sesquiterpene volatiles in pollen grains
Bibliographic record
Abstract
Terpenoid volatiles are important information molecules that enable pollinators to locate flowers and may protect reproductive tissues against pathogens or herbivores. Inflorescences of grapevine (Vitis vinifera L.) are composed of tiny green flowers that produce an abundance of sesquiterpenoid volatiles. We demonstrate that male flower parts of grapevines are responsible for sesquiterpenoid floral scent formation. We describe temporal and spatial patterns of biosynthesis and release of floral volatiles throughout the blooming of V. vinifera L. cv. Cabernet Sauvignon. The biosynthesis of sesquiterpene volatiles, which are emitted with a light-dependent diurnal pattern early in the morning at prebloom and bloom, is localized to anthers and, more specifically, within the developing pollen grains. Valencene synthase (VvValCS) enzyme activity, which produces the major sesquiterpene volatiles of grapevine flowers, is present in anthers. VvValCS transcripts are most abundant in flowers at prebloom stages. Western blot analysis identified VvValCS protein in anthers, and in situ immunolabeling located VvValCS protein in pollen grains during bloom. Histochemical staining, as well as immunolabeling analysis by fluorescent microscopy and transmission electron microscopy, indicated that VvValCS localizes close to lipid bodies within the maturing microspore.
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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 | 0.000 | 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".