Screens Fine Varieties of Haiyuan Foenuculum vulgare Mill. and Studies on it's Volatile Oil Chemical Composition
Bibliographic record
Abstract
Objective:To discusses the difference of volatile oil content and the chemical composition between introduce Foeniculum vulgare Mill varieties to Haivuan varieties,which with high yield and fine quality located in different province,such as Gansu,Inner Mongolia and Shanxi and local variety,provide the reference data for the Haiyuan Foeniculum vulgare Mill.varieties screening and high yield culture technique.Methods:Used the volatile oil received rate(%)as index,degree pulverization of medicine material(A),water quantity(B),immersion time(C),extraction time(D)as inspection factors,used the orthogonal experiment L9(34)to definite volatile oil optimum extraction craft.Used the optimized method extracted the volatile oil of different varieties of Foeniculum vulgare Mill.,and unified GC-MS to determine the chemical composition of its volatile oil and the content separately.Conclusion:The best methods of extracting the volatile oil from the Foeniculum vulgare Mill.was crushing raw material for medicine 40 items,added 12 times water,soaked 1h,withdrew 9h;Volatile oil content of different located Foeniculum vulgare Mill.varieties was different,and the order was:ShanxiHaiiyuanGansuInner Mongolia.And the composition of volatile oil was same,but the content was different slightly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".