Modified sand for the removal of manganese and arsenic from groundwater
Bibliographic record
Abstract
Manganese (Mn) and arsenic (As) are common natural groundwater contaminants and are present at concentrations much higher than the recommended drinking water guidelines. In this study, laboratory investigations were carried out to evaluate the effectiveness of three different types of manganese oxide (MnO2)-coated media in removing manganese and arsenic from water. While all media types were found to be very effective in removing dissolved manganese, the flow rate or contact time was found to have a significant impact on manganese removal. Removal of manganese with ‘synthetic’ manganese-coated media changed from 30% at a flow rate of 8.0 (ml/min)/cm2 to almost 100% at flow rates of about 1 (ml/min)/cm2. Manganese removal was found to increase with increasing manganese content of the filter media and with increasing manganese in the influent water. Removal of arsenic (III) (As3+) in manganese oxide-coated filter media was found to be strongly dependent on pH and phosphate (PO4 3−) concentration. Removal of arsenic decreased from about 80% at pH 7 to almost nil at pH 9. Arsenic removal decreased from 75% in the absence of phosphate to 2.3% in the presence of 10 mg/l phosphate. Results show that manganese oxide-coated media can efficiently remove both manganese and arsenic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".