Biological removal of nitrate with arsenic adsorption
Bibliographic record
Abstract
Nitrate, arsenic and vanadium are all potential groundwater contaminants. Various resource constraints can make conventional treatment methodologies, such as ion exchange and membrane processing, challenging to implement for small utilities. This research examined the potential of biological denitrification withParacoccus denitrificans and adsorption to ferric hydroxide precipitates to simultaneously remove nitrate and either arsenic or vanadium. Continuous bench-scale testing found that average nitrate removal of up to 60% was obtained. The continuous bench-scale testing also found that arsenic and vanadium were both removed by way of adsorption to ferric hydroxide precipitates. The average per cent removal for arsenic and vanadium were 81·5% and 91·1% when 2 mg/L of iron was added. In fact, arsenic concentrations were below 10 μg/L following addition of 2 mg/L of iron. This bench-scale system demonstrates the potential of integrating bio-adsorption for co-contaminant removal, which could hold promise for small communities requiring an innovative approach to simultaneously remove contaminants.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".