Performance of point-of-use devices to remove manganese from drinking water
Bibliographic record
Abstract
A recent epidemiological study reported significant cognitive deficits among children in relation with consumption of water with manganese concentrations in the order of 50-100 ug/L. Concerns for neurotoxic effects of manganese raises the need for evaluating the efficiency of domestic water treatment systems for removal of this metal. The objective of the present study was to determine whether POU devices are efficient at reducing dissolved manganese concentration in drinking water. Various devices were tested according to the NSF 53 protocol for general metals for high pH test water. Based on these assays, the pour-through filters were identified as the most promising POU devices, with dissolved manganese removal greater than 60% at 100% rated capacity, and greater than 45% at 200% rated capacity (influent Mn ≈1,000 μg/L). Under-the-sink filters using cationic exchange resins (i.e., water softeners) were also efficient at removing dissolved manganese but over a shorter operating life. Manganese leaching was also observed beyond their rated capacity, making them less robust treatments. The activated carbon block filters and other proprietary technologies were found to be inappropriate for dissolved manganese removal. Further evaluation of POU devices performance should evaluate the impact of hardness on process performance. The impact of particulate Mn should also be evaluated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".