Enhancing Nitrification at Low Temperature with Zeolite in a Mining Operations Retention Pond
Bibliographic record
Abstract
Ammonium nitrate explosives are used in mining operations at Diavik Diamond Mines Inc. in the Northwest Territories, Canada. Residual nitrogen is washed into the mine pit and piped to a nearby retention pond where its removal is accomplished by microbial activity prior to a final water treatment step and release into the sub-Arctic lake, Lac de Gras. Microbial removal of ammonium in the retention pond is rapid during the brief ice-free summer, but often slows under ice cover that persists up to 9 months of the year. The aluminosilicate mineral zeolite was tested as an additive to retention pond water to increase rates of ammonium removal at 4°C. Water samples were collected across the length of the retention pond monthly over a year. The structure of the microbial community (bacteria, archaea, and eukarya), as determined by denaturing gradient gel electrophoresis of PCR-amplified small subunit ribosomal RNA genes, was more stable during cold months than during July-September, when there was a marked phytoplankton bloom. Of the ammonia-oxidizing community, only bacterial amoA genes were consistently detected. Zeolite (10 g) was added to retention pond water (100 mL) amended with 5 mM ammonium and incubated at 12°C to encourage development of a nitrifying biofilm. The biofilm community was composed of different amoA phylotypes from those identified in gene clone libraries of native water samples. Zeolite biofilm was added to fresh water samples collected at different times of the year, resulting in a significant increase in laboratory measurements of potential nitrification activity at 4°C. A significant positive correlation between the amount of zeolite biofilm and potential nitrification activity was observed; rates were unaffected in incubations containing 1-20 mM ammonium. Addition of zeolite to retention ponds in cold environments could effectively increase nitrification rates year-round by concentrating active nitrifying biomass.
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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) | 0.001 | 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 | 0.001 |
| 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".