Investigation on Influential Factors of Volatile Oil and Main Constituent Content from Curcuma kzoangsiensis S. G. Lee C. F. Liang. In Guangxi Producing Areas
Bibliographic record
Abstract
To quantitatively analyze volatile oil in curcuma kzoangsiensis S. G. Lee C. F. Liang. produced in different places and seasons, and to quantitatively analyze crucumol by gas chromatographic (GC). The volatile oil was distilled by the steam distillation(XD) and the crucumol was determined by GC on a HP-5 column (0.32 mm!`30m, 0.2|m), Inlet -1 temperature 200!aC, FID 250!aC, flow 1.0 ml!/ , splitless. Temperature programming started at 60!aC holding for 4 -1 min, then increased to 210!aC at a rate of 3!aC!/ . The quantity of volatile oil in Curcuma kzoangsiensis S. G. Lee C. F. Liang. in different places and collecting time were detected. The contents of volatile oil and crucumol was the hightest in Bingyang place. The quantity of volatile oil was the richtest in January and February, and the same result was obtained for crucumol. As a result, January and February was the best time for the collection of Curcuma kzoangsiensisS. G. Lee C. F. Liang.. The contents of volatile oil and crucumol should be taken as a standard for the evaluation of the quantity 0f curcuma phaeocaulis val.. and the determination of its collecting time.
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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".