Floral volatile composition of four species of <i>Vaccinium</i><sup>1</sup>This article is part of a Special Issue entitled “A tribute to Sam Vander Kloet FLS: Pure and applied research from blueberries to heathland ecology”.
Bibliographic record
Abstract
The floral volatile composition of four species of Vaccinium was profiled to asses the diversity of the floral chemistry within this genus. Flowers of Vaccinium angustifolium Aiton, Vaccinium varingiaefolium Miq., Vaccinium arboreum Marsh., and Vaccinium poasanum Donn. Sm. were sampled from three or more plants, and volatiles were collected and analyzed by gas chromatography – mass spectroscopy. A total of 45 volatile compounds were detected in the air (headspace) surrounding flowers including 40 from V. angustifolium, 34 for V. varingiaefolium, 37 for V. arboreum, and 17 for V. poasanum. Of the volatile compounds identified, 34 were terpenoids, 6 were benzoids and phenylpropanoids, 4 were aliphatics, and 1 was a miscellaneous cyclic compound. Terpenoids accounted for 98%, 80%, 76%, and 67% of the total volatiles for V. angustifolium, V. varingiaefolium, V. arboreum, and V. poasanum, respectively. The most abundant volatile compound emitted from the flowers of each species was α-pinene for V. angustifolium (23%) and V. arboreum (63%), methyl benzoate for V. varingiaefolium (18%), and ethyl benzene for V. poasanum (21%). Substantial variation was observed in the floral volatile composition of these four Vaccinium species, which may reflect their diverse ecological origins.
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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) | 0.000 | 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".