Nitrogen and cyanide compound removal from gold mine impacted water using an anammox bioreactor
Bibliographic record
Abstract
The use of ammonium-nitrate fuel/oil (ANFO) explosives and cyanide in the gold mining industry can lead to elevated concentrations of nitrogen compounds in mine-impacted water, often requiring treatment before discharge. Recent limits on the concentration of ammonia added to the Canadian Metal and Diamond Mining Effluent Regulation (MDMER) further stress the need for cost-effective solutions to remove nitrogen from effluents. Nitrogen compounds are typically removed through two biological processes: 1) aerobic oxidation of ammonia, and 2) anaerobic reduction of nitrate. These processes can be costly due to the need for separate reactors, addition of a carbon source and aeration. Anaerobic ammonium oxidation (anammox) bacteria solve this issue by simultaneously converting ammonia and nitrite to nitrogen gas in a single anaerobic autotrophic process. Despite the successful application of anammox to wastewater treatment plants, little research has been done on its application to mine effluents. Here, we present an anammox-containing culture with an emphasis on its nitrogen removal capabilities as well as the microorganisms identified to carry out the metabolism. Results of our laboratory application of the culture to remove nitrate, ammonia and cyanide compounds from a gold mine effluent are presented.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".