Performance of reverse osmosis and manganese greensand plants in removing naturally occurring substances in drinking water
Bibliographic record
Abstract
The Water Security Agency has a legislative authority to regulate water treatment systems and enforce standards with respect to drinking water quality in the Province of Saskatchewan. A number of communities in Saskatchewan which depend on groundwater as a source for drinking water have reported high levels of naturally occurring substances, such as arsenic, uranium and selenium, in their raw water. These communities continue to upgrade their systems by installing new or retrofitting with treatment units, such as reverse osmosis (RO) and manganese greensand (MGS) filters to reduce the levels of naturally occurring substances in finished water. In order to assess the treatment performance of these systems, a study was initiated to collect samples from 20 communities across Saskatchewan and analyse naturally occurring substances in raw and finished water. The study focused on the removal efficiency and the effect of parameters such as sulfate, total dissolved solids, and hardness on the removal efficiency. The paper includes discussion on the results and analysis of sampling/research studies conducted to assess the performance of treatment systems. Results showed that RO plants are effective in removing uranium and MGS are effective in removing arsenic from drinking water.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".