ARTEMISININ CONTENT ON ARTEMISIA ANNUA L. TREATED BY GLORIOSA SUPERBA SEEDS’ WATER EXTRACT
Bibliographic record
Abstract
Objective: The aim of this study was to determine the artemisinin content on Artemisia annua L. treated by water extract of Gloriosa superba seeds.Methods: G. superba seeds obtained naturally on Krakal Beach, Gunung Kidul, and extraction used a maceration method by water solvent (1:1). A. annua L. sprouts were obtained from B2P2TOOT Tawangmangu. Treatment variables done on sprouts using various water extract concentration of G. superba seeds and soaking time on A. annua L. sprouts. Determination of artemisinin content in leaf extract of A. annua L. was done using KLT-densitometric method with n-hexane:ethyl acetate (4:1) as mobile phase.Result: The result showed that artemisinin content in plant treatment of G. superba seed water extract was higher (9.78 μg/μl [±3.21]–16.60 μg/μl [±1.39]) compared to control plants (6.39 μg/μl [±1.40]). The concentration water extract of G. superba seed affected the level of artemisinin in the treatment plant. On the other hand, the soaking of A. annua L. sprouts using the water extract of G. superba seed did not affect the level of artemisinin content.Conclusion: Artemisinin content in treatment plant by G. superba seed water extract treatment was higher compared to control plants.
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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 | 0.000 | 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 | 0.001 |
| 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".