Studi Efektivitas Koagulan Kitosan-Kapur Dalam Menurunkan COD, MBAS dan Fosfat pada Limbah Laundry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it