Low Cost Water Disinfectant System Using Solar Energy
Bibliographic record
Abstract
Solar disinfection unit is unsophisticated, efficient and reasonably priced water treatment process appropriate for use in developing countries. Water was filtered through cloth, net and coconut husk to remove any suspended particles in water which would directly increase the efficiency of solar disinfection. The filtered water is then transferred in solar disinfection unit. Water with Escherichia coli as indicator organism was filled in the solar disinfection unit comprising of four polyethylene terepthalate (PET) plastic bottles joined together with PVC pipes. These bottles were kept in direct sunlight for 12-48 hours. Weather conditions and solar radiation were obtained using different programs. Solar radiations and elevated temperature destroyed the indicator organism efficiently. Health peril of chemical released in water was well thought-out and was determined to be safe with respect to human consumption. The efficiency of solar disinfection was augmented by use of semi-conductor titanium dioxide (TiO2) which reduced the time for exposure up to 90%. The temperature increase and dissolved oxygen decrease in the disinfection process which was overcome by retaining this water in traditional earthenware water storage vessels (mutka). Allowing accessibility to better life through improved quality of water.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".