Chemical composition and antibacterial activities of<i>Juniperus horizontalis</i>essential oil
Bibliographic record
Abstract
Eryiğit, T., Okut, N., Ekici, K. and Yildirim, B. 2014. Chemical composition and antibacterial activities of Juniperus horizontalis essential oil. Can. J. Plant Sci. 94: 323–327. In recent years, the screening of antibacterial activity has been the subject of much research, and the antibacterial activity of essential oils could be a promising subject for future investigation. In this study, the antibacterial activities of Juniperus horizontalis essential oils and their components were investigated. Essential oils were isolated using the hydro-distillation method and their components were analyzed by gas chromatography–mass spectrometry (GC-MS). The main compounds found in the oil of leaves were linalool (33.76%), P-cymene (23.20%), gamma-terpinene (8.67%), trans-sabinene hydrate (8.59%), thyme camphor (8.49%), carvol (5.08%) and borneol (4.22%). Juniperus horizontalis essential oils were evaluated for antibacterial activity against six bacterial strains using the disc diffusion method. The results indicate that the essential oil of J. horizontalis shows variable and significant antibacterial activities against the six tested bacteria species. The diameters of the inhibition zones formed for bacteria were measured. The lowest inhibition zone was 12 mm (sensitive) against Pseudomonas aeruginosa ATCC 10145 and the highest zone was 32 mm (extremely sensitive) against Enterococcus faecalis ATCC 29212.
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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) | 0.001 | 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".