Chemical Composition and Enantiomeric Distribution of the Essential oil of <i>Larix occidentalis</i> Nutt
Bibliographic record
Abstract
Background/Objective: Larix occidentalis (Pinaceae) is a tree native to the mountains of northwestern United States and southwestern Canada. Indigenous peoples used this tree as part of their traditional medicine to treat various ailments, including coughs, colds, arthritis, and wounds. As part of our interest in essential oils of coniferous tree species and the potential utilization as fragrances, we have obtained the foliar essential oil of L. occidentalis from Idaho, USA, which complements previous reports from Canada. Methods: The foliage from three individual trees growing on Brundage Mountain, Idaho, were collected; the essential oils obtained by hydrodistillation, and the essential oils characterized by gas chromatographic methods, including enantioselective gas chromatography – mass spectrometry. Results: The yellow essential oils were obtained in yields of 1.25-1.77%. The major components in the essential oils were α-pinene (26.5 ± 5.4%), β-pinene (14.9 ± 2.5%), α-terpineol (8.3 ± 1.2%), and δ-3-carene (6.6 ± 2.9%). The (−)-enantiomers were dominant for camphene (84.1 ± 6.6%), β-pinene (97.7 ± 0.4%), β-phellandrene (80.6 ± 3.2%), borneol (86.2 ± 0.9%), and α-terpineol (85.4 ± 4.6%); the (+)-enantiomer was the only enantiomer observed for δ-3-carene. Conclusions: This is the first investigation of L. occidentalis essential oil from Idaho. The major components are consistent with ethnopharmacological uses of the plant. Additional work is needed to more clearly define the essential oil compositions of North American, European, and Asian Larix species.
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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".