Biological Manganese Oxidation by Pseudomonas putida in Trickling Filters
Bibliographic record
Abstract
Manganese (Mn) is considered a nuisance chemical in drinking water. Manganese causes problems with staining, foul odor, undesirable tastes, and can be corrosive to pipelines. The United States Environmental Protection Agency (US EPA) recommends a secondary maximum contaminant level for Mn below a concentration of 0.05 mg/L. Currently manganese contaminated water is typically treated using expensive and potentially harmful oxidizing agents. Biological treatment techniques have been researched as a viable alternative for removing undesired chemicals from drinking water. In this study, bench scale trickling filters were constructed to compare the Mn removal efficiency between biochemical and abiotic processes. Glass beads between three and five millimeters in diameter were used as the solid media in the trickling filters with and without inoculation of a Mn oxidizing bacterium, Pseudomonas putida. Manganese oxidation and removal was found to be significantly greater in trickling filters with Pseudomonas putida biofilms after startup times of only 48 hours. Mn oxidation in Pseudomonas putida inoculated trickling filters was up to 75% greater than non-inoculated filters. One dimensional advection dispersive models were formulated to describe the transport of Mn in trickling filter porous media. Using the data collected in the experiments, the model predicted that that an average of 10 mg/L of influent Mn (II) concentration can be decreased by 78.56% with a filter depth of only 10 cm. The rapid startup time and the high Mn removal capacity of trickling filters inoculated with Pseudomonas putida can potentially become a mainstream treatment system in conjunction with sand filters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".