Predicting manganese and iron precipitation in drinking water biofilters
Bibliographic record
Abstract
Abstract Manganese is removed from drinking water because of aesthetic, health, and corrosion concerns. Like iron, manganese removal in biofilters depends on homogeneous, heterogeneous, and biological processes. This study aims to add further understanding to the importance of homogeneous processes for manganese removal in drinking water biofilters. Data collected from 10 groundwater biofilters across filter depth were analyzed using Pourbaix diagrams and thermodynamic modelling software (PHREEQC). Both methods predicted that iron would precipitate in all samples, but the predictions differed for manganese. The software approach provided a more precise look into the minerals predicted to form by homogeneous oxidation, allowing for deeper interpretation of biofilter oxidation–reduction (redox) conditions. Results suggest that multiple manganese removal mechanisms occur across biofilters, but homogeneous manganese oxidation may be essential to meet new Canadian guidelines. Utilities operating manganese removing biofilters should measure redox conditions to determine if changing biofilter pretreatment could decrease effluent manganese concentrations. Article Impact Statement Results of this study suggest that homogeneous manganese oxidation may be important for achieving the low effluent concentrations required by Canadian guidelines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".