Examination of some quality analysis carried out to the determination of standardization of St. John's Wort (Hypericum perforatum L.) oil used in traditional medicine applications
Bibliographic record
Abstract
St. John's Wort (Hypericum perforatum L.) plant is among the medicinal plants used as pharmaceutical raw material with traditional methods for more than 2000 years. St. John's Wort plant and its medicinal oil (maserate) obtained from it, usage area is increasing day by day in the food, pharmaceutical and cosmetic industries thanks to the important bioactive components. The pharmacologically important component of the plant is known as hypericin. This study was carried out in Selcuk University, Faculty of Agriculture, Medical and Endemic Plants Training and Research Farm, and examined the effect of different holding environments (sun, shade), harvest periods (25% flowering period, 50% flowering period and full flowering period) and drying methods (wet herb, faded herb and dried herb) on the amount of hypericin in St. John's Wort and its medicinal oil. According to the results of the analysis, the highest amount of hypericin in St. John's Wort herb was determined as 0.32% in full flowering period. The highest amount of hypericin in St. John's Wort oils was determined as 271.92 mg/L in fresh herb during the full flowering period. According to the results of the research, the harvesting periods are effective on the amount of hypericin, and it can be suggested that the wet herb, which is harvested in full bloom and hold, should be macerated with olive oil in the sun to obtain St. John's Wort oil with high hypericin content.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".