Nutrient Impacts on Biogenic Chemical Flux in an End Pit Lake Reclamation Scenario
Bibliographic record
Abstract
Ebullition of biogenic gases, primarily methane (CH 4 ), might affect the trajectory of end pit lake (EPL) reclamation. We investigated the influence of nutrients (N with or without P) on methanogenesis and the resulting chemical flux from underlying fluid fine tailings (FFT) to cap water. Anaerobic 10 L columns were filled with FFT amended with a mixture of hydrocarbons ( n -alkanes, iso -alkanes, and monoaromatics from C 6 –C 10; HC columns) and capped with process water. Some amended FFT were further amended with N (HCN columns) or N and P (HCNP columns). A microbial community dominated by Desulfobacterota and Desulfotomaculales plus acetoclastic and hydrogenotrophic methanogens depleted hydrocarbons at different rates concomitant with CH 4 production in the order HCN > HC > HCPN, indicating stimulation of methanogenesis by N and inhibition by P amendments. Microbial processes transformed FFT minerals (Fe III to Fe II minerals and dissolution of carbonates), mobilized ions, and trace elements (Ca 2+, Mg 2+, K +, HCO 3 –, Sr, and Ba) in FFT porewater and densified FFT and induced chemical flux to cap water. Other dissolved trace elements (As, Sb, and Mo) decreased in porewater and cap water during methanogenesis. The results provide novel information about nutrients’ effect on methanogenesis and associated chemical flux to inform predictions about sustainable management of EPL.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".