Hydrodistillation Extraction Kinetics Regression Models for Essential Oil Yield and Composition in Juniperus virginiana, J. excelsa, and J. sabina
Bibliographic record
Abstract
The chemical profile and antioxidant capacity of Juniperus virginiana, J. excelsa, and J. sabina essential oil (EO) fractions as a function of time was the subject of this study. The hypothesis was that, capturing EO in sequential timeframes during hydrodistillation would generate fractions containing unique compositions and antioxidant capacity. In J. virginiana, the highest limonene (43%) was found in the 0–5 min oil fraction, with safrole (37%) being highest in the 10–20 and 20–40 min fractions, and elemol (34%) being highest in the 160–240 min fraction. In J. excelsa, α-pinene (34-36%) was the highest in the 0–5 min fraction and in the control (non-stop 0–240 min distillation) oil, limonene (39%) was the highest in the 0–10 min fractions and cedrol (50-53%) was the highest in the 40–240 min fractions. In J. sabina, sabinene (80%) was highest in the 0–3 min fraction. The highest antioxidant capacity of J. virginiana was demonstrated by the 5–10 min fraction; the one in J. sabina by the 3–10 min fraction; and, the one in J. excelsa, by the control. The kinetics regression models that were developed can predict EO composition of the three juniper species eluted at different timeframes. Various industries could benefit from the results from this study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".