PERANCANGAN PENGOLAHAN AIR MINUM TENAGA SURYA KAPASITAS 50 M3/HARI (Dengan Menggunakan Proses Biofiltrasi Dan Ultrafiltrasi)
Bibliographic record
Abstract
Drinking water supply for public is big deals for the improvement of environmental and society health. An effort from the government to provide the drinking water supply is to provide a society based of drinking water treatment plant. Some obstacles that have come for the plant in remote areas are the difficulties of getting chemical material for the plant and the lack of energy supply. Drinking water treatment that use chemical materials has a big cost to provide the chemicals. Beside the high cost of treatment, the using of chemicals will harmful the drinking water, because it usually generates such carcinogenic materials. The biofiltration-ultrafiltration treatment is one of the best solutions to replace treatment plant that using chemicals. It has a low cost operation and is more safe than the water treatment that using chemicals. Generator-set (genset), a power generator that easy to get and operate, has uneconomical operation cost. By now, there were many studies proved that in remote areas, the using of Solar Power Plant is more economic than a genset. For those reasons, in this article is designed a solar powered 50 m3/Day drinking water treatment (biofiltration-ultrafiltration process). The result from the design is the solar power plant that can supply the treatment plant for 8 hours. Keywords: drinking water, solar power, genset, biofiltration, ultrafiltration.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".