Monitoring, assessing and recommending the quality potable water on UIA campus
Bibliographic record
Abstract
Drinking water quality is of utmost importance both to public health and to conservation of water bodies. Maintenance of quality standards for potable water inhibits the spread of any waterborne disease and to protect receiving water quality. While achieving these water quality standards, it it necessary to consider the treatment that is required for such standards that is supplied to UIA Gombak campus by creating a database of life cycle inventory parameters. These parameters result from a life cycle assessment conducted on the system according to pre-determined boundaries. The system included the supply and distribution of potable water to UIA Gombak campus. For each parameter in the system, common treatment trains were developed using information from literature reviews prior to this study. The assessment required a comprehensive literature review of studies done prior to this one it also incorporated some of their analysis. In recent times there have been complaints from different quarter in UIA Gombak about the taste, smell, and color of the water supplied. At the end of this study the conclusion was reached that the water in UIA is safe to drink all the parameters were made how to improve the water standards.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".