Pengaruh Perbandingan Konsentrasi Asam Sitrat-Malat terhadap Karakteristik Granul Effervescent Daun Katuk (Sauropus androgynus L. Merr)
Bibliographic record
Abstract
The purposes of this research was to determine the effect of concentration ratio of citric-malic acid on the characteristics of the effervescent granule of katuk leaves and to proper concentration ratio of citric-malic acid to get the effervescent granule of katuk leaves with the best characteristics. The research used Completely Randomized Design (CRD) with the treatment of concentration ratio of citric-malic acid consisting of 5 levels: 5% : 25%, 10% : 20%, 15% : 15%, 20% : 10%, dan 25% : 5%. The treatment was repeated 3 times in order to obtain 15 experimental units. The data were analyzed statistically using the variance test and if the treatment had a significant effect to observed variables, it was continued with Duncan's Multiple Range Test. The result showed that concentration ratio of citric-malic acid had a significant effect (P<0,05) on moisture content, ash content, flow time, soluble time, high foaming, total flavonoids, and antioxidant capacity. The concentration ratio of 5% citric acid and 25% malic acid was the best treatment to produce effervescent granule of katuk leaves with the moisture content 5.67%, ash content 23.84%, pH 5.88, soluble time 20.69 seconds, flow time 0.80 seconds, high foaming 3.20 cm, total flavonoids 21.04 mgQE/100g powder, antioxidant capacity 16.51 mgGAEAC/100g powder sensory hedonic of the color was liked, the aroma was liked, the taste was liked and the overall acceptance was liked.
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) | 0.003 | 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".