Affordable filtration technology of safe drinking water for rural Newfoundland and Labrador
Bibliographic record
Abstract
The main objective of this study is to design a cost-effective filtration system to reduce natural organic matter (NOM) in the intake water source and also to remove trihalomethanes (THMs) and haloacetic acids (HAAs) in the drinking water systems of Torbay and Pouch Cove communities near St. John’s. To reduce the concentration of THMs and HAAs, a series of experiments were conducted on tap water using an inexpensive adsorbent. The results showed more than 95% removal of THMs and 35% of HAAs in the Pouch Cove drinking water using activated carbon. Another test was conducted with clean carbon without activation which shows significant removal of chloroform, bromodichloromethane, and bromoform in the THMs group and a high percentage removal of bromchloracetic acid, dichloroacetic acid, trichloroacetic acid, and dibromoacetic acid in the HAA group. Clean carbon was also used to remove total organic carbon (TOC) in the intake water source before chlorination. The results showed more than 92% removal to TOC from Pouch Cove and 65% removal from the Torbay intake water. The results showed that the formation potential of THMs and HAAs are significantly reduced due to low TOC values in the filtered water and low-cost adsorbent can be used as an effective adsorbent to supply safe drinking water to rural communities.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".