Acidic Microenvironments in Waste Rock Characterized by Neutral Drainage: Bacteria–Mineral Interactions at Sulfide Surfaces
Bibliographic record
Abstract
Microbial populations and microbe-mineral interactions were examined in waste rock characterized by neutral rock drainage (NRD). Samples of three primary sulfide-bearing waste rock types (i.e., marble-hornfels, intrusive, exoskarn) were collected from field-scale experiments at the Antamina Cu–Zn–Mo mine, Peru. Microbial communities within all samples were dominated by neutrophilic thiosulfate oxidizing bacteria. However, acidophilic iron and sulfur oxidizers were present within intrusive waste rock characterized by bulk circumneutral pH drainage. The extensive development of microbially colonized porous Fe(III) (oxy)hydroxide and Fe(III) (oxy)hydroxysulfate precipitates was observed at sulfide-mineral surfaces during examination by field emission-scanning electron microscopy-energy dispersive X-ray spectroscopy (FE-SEM-EDS). Linear combination fitting of bulk extended X-ray absorption fine structure (EXAFS) spectra for these precipitates indicated they were composed of schwertmannite [Fe8O8(OH)6–4.5(SO4)1–1.75], lepidocrocite [γ-FeO(OH)] and K-jarosite [KFe3(OH)6(SO4)2]. The presence of schwertmannite and K-jarosite is indicative of the development of localized acidic microenvironments at sulfide-mineral surfaces. Extensive bacterial colonization of this porous layer and pitting of underlying sulfide-mineral surfaces suggests that acidic microenvironments can play an important role in sulfide-mineral oxidation under bulk circumneutral pH conditions. These findings have important implications for water quality management in NRD settings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".