Microbially driven acidity generation in a tailings lake
Bibliographic record
Abstract
ABSTRACT In situ characterization and geochemical modelling of acid generation in a mine tailings lake (Moose Lake, ON, Canada) over a 2‐year period (2001–2002; surficial lake pyrrhotite slurry disposal initiated in 2002) show that bacteria significantly impact acidity behaviour through particle‐associated S oxidation and that they do so under conditions that differ from those controlling abiotic pathways. Seasonal epilimnetic pH decreases occurred in both years, decreasing from approximately 3.5 in May to 2.8 by September (2001) or July (2002). Epilimnetic acid generation rates were depth‐dependent, with maximal rates observed not at the surface of lake where O 2 concentrations were highest, but rather within a geochemically reactive zone (approximately 1 m thick) of steep, decreasing O 2 gradients and dynamic Fe and S geochemistry in the lower epilimnetic region of the lake. Acid generation occurred dominantly through particle rather than aqueous pathways, but model predictions of acid generation via abiotic pyrrhotite oxidation involving either O 2 or ferric iron (Fe 3+ ) predicted neither the observed rates nor the depths at which maximal rates occurred. In contrast, model predictions based on microbial pathways involving both O 2 and ferric iron (Fe 3+ ) agreed extremely well with both the observed depth profile of H + generation and the observed rates at any given depth. Imaging showed extensive microbial colonization of epilimnetic‐associated pyrrhotite particles commonly with significant biofilm formation. FISH (fluorescence in situ hybridization) probing of the community in both pelagic and particle compartments indicated mixed communities occurred in both, and that Acidithiobacillus spp. accounted for 2–46% of the total community in either compartment. Initiation of pyrrhotite slurry discharge at the lake surface in 2002 was accompanied by a relative increase in the number of particle‐associated microbes, as well as a relative proportional decrease of Acidithiobacillus spp. in the total microbial community. Given the widespread occurrence of bacteria across mining environments, the implications of our results extend beyond this specific site and provide new insight into bacterially driven processes contributing to bulk system characteristics which are not currently well constrained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".