UJI KINERJA PENGOLAHAN AIR SIAP MINUM DENGAN PROSES BIOFILTRASI, ULTRAFILTRASI DAN REVERSE OSMOSIS (RO) DENGAN AIR BAKU AIR SUNGAI
Bibliographic record
Abstract
Water is a very basic need for humans, especially for cooking and drinking. With the rapid growth of population in particular need of clean water for the community also increased in numbers. The problem is with the poor quality of raw water for drinking water, then in addition to expanding its production costs, the result is often less good. One of the problems or issues that are often found in drinking water in the world these days that is the emergence of compounds called Trihalomethanes or THMs abbreviated, as a side effect of the disinfection process with a chlorine gas or hypochlorite compounds.Currently, to removal organic pollutants, ammonia, detergents, odor and other micro pollutants in drinking water, PAM is usually used by the process of manufacturing processes using adsorbsi Powder Active Carbon Adsorption, continued with physicals processing is the process of coagulation, flocculation, sedimentation and disinfection with chlorine. With increasingly high prices of powdered activated carbon, coagulant and flocculant chemicals, then the cost of treating drinking water to be increasing. To solve the problem above, one alternative is to develop clean water treatment technologies using a combination of biofiltration and ultrafiltration process, and to produce drinking water to proceed with processing using the process of reverse osmosis. Within the combination of biofiltration, ultrafiltration and reverse osmosis processes to treat the river water can be produced the drinking water with a very good quality without the use of chemicals for coagulation-flocculation process, and operational costs are relatively low. Key words : Air siap minum, biofiltrasi, ultrafiltrasi, reverse osmosis
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".