Phytochemical Analysis of Methanolic Extract of Jordanian Melissa officinalis L.
Bibliographic record
Abstract
The methanolic extract obtained from leaves, steam and seeds of M. officinalis growing in Karak city-Jordan, was investigated, discussed for its phytoconstituents for the first time and analyzed by GC-MS instrument with BSTFA and Heptan solvents. Plant parts were collected during the spring semester 2018. Results showed that leaves extract have twenty one major and minor natural compounds available in all parts. Six of them which were above 1 % have been found in the extract of leaves more than other parts of the plant. Leaves extract are a good source of various phytoconstituents. These natural compounds gave leaves an important role to use it as anticancer and sedative due to the presence of Palmitic acid and polar compounds. Leaves are much interesting part due to the availability of these bioactive compounds as major and minor compounds more than other parts. Thus, the isolation of leaves phytochemical compounds will give fruitful results for further detailed 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".