Studi Efektivitas Koagulan Kitosan-Kapur Dalam Menurunkan COD, MBAS dan Fosfat pada Limbah Laundry
Bibliographic record
Abstract
Abstract. Environmental pollution that is often encountered in daily life that comes from laundry waste. Laundry waste includes pollutants or substances that pollute the environment because in it there is a substance called linear alkylbenzene sulphonate (LAS). LAS is a detergent that is classified as hard to brake down by microorganisms (non-biodegradable) so that it can cause environmental pollution. One method that is often used in laundry wastewater treatment is coagulation using chitosan and lime as a coagulant. The purpose of this study was to analyze the efficiency and effectiveness in reducing pollutant levels in laundry waste using chitosan-lime coagulant. This study used a completely randomized design with 200 mg/L chitosan and 0.1-0.5 g lime. The test parameters used were COD, MBAS, and phosphate. Data were analyzed using calculation of efficiency and effectiveness of reduction, linear regression, and one-way ANOVA test. The results showed that under the best conditions, chitosan 200 mg/L and lime as much as 3.5 g resulted in a reduction efficiency of 68.52%, 9.15%, and 92.44%. Chitosan-lime is effective in reducing MBAS and phosphate levels to quality standard, but chitosan-lime coagulant is less effective in reducing COD levels because it still exceeds the the established quality standards
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".