Productivity, Oil Content, Composition, and Bioactivity of Oil-bearing Rose Accessions
Bibliographic record
Abstract
Rose oil production worldwide is based on different oil-bearing Rosa species. This 4-year study determined the essential oil content, constituents, and morphologic/phenologic characteristics of 25 varieties, chemotypes, and hybrids belonging to five Rosa species ( R. damascena Mill., R. gallica L., R. centifolia L., and R. alba L.). Limits of variation of these indices were established for each variety, chemotype, and hybrid group. The essential oil content of R. damascena varied from 0.032% to 0.049% and that of hybrid roses from 0.037% to 0.05%. The highest essential oil content was found in R. damascena accession Svejen 74 and the lowest in R. alba . Within R. damascena , the weight of single flowers varied from 2.09 to 3.44 g, the number of petals from 22 to 28, the height of the plants from 61 to 128 cm, and the diameter of bushes from 53 to 118 cm. R. centifolia had the largest flowers. The essential oil of the various species showed moderate to no antimicrobial activity at 50 μg/mL and no significant antibacterial, antifungal, antileishmania, or antimalarial activity at this concentration. All the tested species and accessions could be grown in Bulgaria (and possibly in southeastern Europe and the northern Mediterranean) and provide comparable productivity to the traditional species R. damascena . Wide variations occurred in essential oil content and constituents and morphologic/phenologic characteristics of the tested Rosa species and accessions. The availability of various species and chemotypes within specific species offer an opportunity for production of oil-bearing roses and essential oils to meet market requirements of specific rose oils.
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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 | 0.001 |
| 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".