Development of effective and low-cost water treatment method for micro systems in British Columbia, Canada
Bibliographic record
Abstract
In this study, an effective and low-cost water treatment system is developed. At first, an investigation combining published literature, available information through different health regions and raw water sample data from participating communities was done. Through that investigation, a critical water contamination problem, experienced by the rural, remote, First Nation communities was identified, which is Manganese. Then a treatment system was developed to effectively remove Manganese from the source water. Greensand plus was used as a filtration media and the prototype was designed to best suit the need of the community residents to remove Manganese in raw water. The lab scale and prototype experiments were designed using design expert software; adsorption isotherm and kinetics study were also conducted. The prototype was able to reach a removal efficiency 96.50% which can effectively treat source water levels found in the raw water sample. Therefore, a fit-for-purpose solution is developed to remove Manganese from raw water, which is cost effective, easy to use and maintain. Cost effectiveness is demonstrated through comparison of the developed system with available typical clean water supply strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".