pH and Organic Carbon Dose Rates Control Microbially Driven Bioremediation Efficacy in Alkaline Bauxite Residue
Bibliographic record
Abstract
Bioremediation of alkaline tailings, based on fermentative microbial metabolisms, is a novel strategy for achieving rapid pH neutralization and thus improving environmental outcomes associated with mining and refining activities. Laboratory-scale bioreactors containing bauxite residue (an alkaline, saline tailings material generated as a byproduct of alumina refining), to which a diverse microbial inoculum was added, were used in this study to identify key factors (pH, salinity, organic carbon supply) controlling the rates and extent of microbially driven pH neutralization (bioremediation) in alkaline tailings. Initial tailings pH and organic carbon dose rates both significantly affected bioremediation extent and efficiency with lower minimum pHs and higher extents of pH neutralization occurring under low initial pH or high organic carbon conditions. Rates of pH neutralization (up to 0.13 mM H + produced per day with pH decreasing from 9.5 to ≤6.5 in three days) were significantly higher in low initial pH treatments. Representatives of the Bacillaceae and Enterobacteriaceae, which contain many known facultative anaerobes and fermenters, were identified as key contributors to 2,3-butanediol and/or mixed acid fermentation as the major mechanism(s) of pH neutralization. Initial pH and salinity significantly influenced microbial community successional trajectories, and microbial community structure was significantly related to markers of fermentation activity. This study provides the first experimental demonstration of bioremediation in bauxite residue, identifying pH and organic carbon dose rates as key controls on bioremediation efficacy, and will enable future development of bioreactor technologies at full field scale.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".