Does Addition of Phosphate and Ammonium Nutrients Affect Microbial Activity in Froth Treatment Affected Tailings?
Bibliographic record
Abstract
We examined greenhouse gas (GHG) production upon the addition of ammonium and phosphate to mature fine tailing (MFT) samples from Alberta’s Pond 2/3 (at 5 and 15 m) and Pond 7 (12.5 m) in microcosm studies. The methane production rate in unamended Pond 2/3 MFT correlated with sample age; the production rate was higher in the less dense, more recently discharged MFT samples and lower in the denser, deeper sample. Adding small amounts of naphtha increased methane production, but there was no correlation with increasing naphtha, indicating that naphtha may partition into bitumen, reducing its bioavailability. Although non-detectable phosphate and low ammonium in the pore water indicate that these nutrients were potentially limiting microbial activity, their addition did not significantly affect methanogenesis but somewhat enhanced sulphate and nitrate reduction. Neither ammonium nor phosphate were detected in the pore water when added at low concentrations, but when added at high concentrations, 25–35% phosphate and 30–45% ammonium were lost. These ions likely sorbed to MFT minerals such as kaolinite, which have microbial activity governed by phosphate/ammonium desorption. Hence, multiple limitations affected microbial activity. Sulphate was less effective than nitrate was in inhibiting methanogenesis because H2S may be a less effective inhibitor than NOx− intermediates are, and/or H2S may be more easily abiotically removed. With nitrate reduction, N2O, a potent GHG was produced but eventually metabolized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".