Essential Oil Yields and Quality of Fennel Grown in Nova Scotia
Bibliographic record
Abstract
Field and laboratory experiments were conducted at two sites in Nova Scotia during 2001 and 2002 to assess the potential to grow fennel ( Foeniculum vulgare Mill.) as an essential oil crop in the Maritime region of Canada. Three cultivars—`Shumen', `Berfena', and `Sweet Fennel'—and two seeding dates—24 May and 8 June—were evaluated. Essential oil yields and composition were determined and compared to commercially available fennel essential oil from the U.S. The highest herbage yields were produced by `Shumen' from the earlier seeding date. Essential oil content and yields were lowest in `Sweet Fennel' and highest in `Shumen'. The major component of the essential oil was anethole, 47% to 80.2%. Other major components of the essential oil were methyl chavicol, fenchone, α-phellandrene, α-pinene, ortho cymene, β-phellandrene, fenchyl acetate, β-pinene, and apiole. The essential oil composition was unique to each cultivar. The highest methyl chavicol content was in `Shumen', while the highest concentration of phellandrene, fenchyl acetate and apiole were detected in `Sweet Fennel' oil. Fenchone, ortho cymene, β-pinene, α-phellandrene, and α-pinene were the highest in `Berfena'. The composition of the oil was similar to the commercially purchased oil and met industry requirements of oil composition. The results suggest there is potential to grow fennel as an essential oil crop in Nova Scotia.
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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 | 0.001 | 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".