Effect of Piper Betle Linn Extract Concentration and Contact Time on Reducing Bacillus Subtilis and Bacillus Stearothermophilus in Medical Waste
Bibliographic record
Abstract
Non-optimized medical waste treatment can produce biological residues of Bacillus subtilis and Bacillus stearothermophilus bacteria, which are agents of various diseases.The first purpose of this study was to determine the difference in the length of contact time and dose of green betel leaf (Piper betle Linn.) extract on the number of Bacillus sp. in the medical waste recycling process.This study's second purpose was to determine the total Bacillus subtilis and Bacillus stearothermophilus reduction after treatment.This study's research design was an experimental design (the after-only design).The concentrations used were 0.03%, 0.05%, 0.07%, 0.3%, 0.5%, 0.7%, 3%, 5% and 7%, and contact times of 15, 30, and 45 minutes with 144 samples each.The ANOVA (one-way) test results showed that there were no differences in the length of contact time and dose of Piper betle Linn.extract as a disinfectant on the number of Bacillus subtilis in the medical waste recycling process.The smallest amount of Bacillus sp. was found at a concentration of 0.05% green betel leaf immersion (330 colonies/ml), and the largest colonies occurred at a concentration of 3% immersion (658 colonies/ml).The contact time and concentration of green betel leaves had no difference on the number of Bacillus sp., but the concentration of 3% showed optimal results in reducing these bacteria.Antimicrobial Piper betle Linn.content can be developed for further research in the removal of bacteria or other parasites.
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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 | 0.000 |
| 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".