Elemental Sulfur and Organic Matter Amendment Drive Alkaline pH Neutralization and Mineral Weathering in Iron Ore Tailings Through Inducing Sulfur Oxidizing Bacteria
Bibliographic record
Abstract
Mineral weathering and alkaline pH neutralization are prerequisites to the ecoengineering of alkaline Fe-ore tailings into soil-like growth media (i.e., Technosols). These processes can be accelerated by the growth and physiological functions of tolerant sulfur oxidizing bacteria (SOB) in tailings. The present study characterized an indigenous SOB community enriched in the tailings, in response to the addition of elemental sulfur (S 0 ) and organic matter (OM), as well as resultant S 0 oxidation, pH neutralization, and mineral weathering in a glasshouse experiment. The addition of S 0 was found to have stimulated the growth of indigenous SOB, such as acidophilic Alicyclobacillaceae, Bacillaceae, and Hydrogenophilaceae in tailings. The OM amendment favored the growth of heterotrophic/mixotrophic SOB (e.g., class Alphaproteobacteria and Gammaproteobacteria ). The resultant S 0 oxidation neutralized the alkaline pH and enhanced the weathering of biotite-like minerals and formation of secondary minerals, such as ferrihydrite- and jarosite-like minerals. The improved physicochemical properties and secondary mineral formation facilitated organo-mineral associations that are critical to soil aggregate formation. From these findings, co-amendments of S 0 and plant biomass (OM) can be applied to enhance the abundance of the indigenous SOB community in tailings and accelerate mineral weathering and geochemical changes for eco-engineered soil formation, as a sustainable option for rehabilitation of Fe ore tailings.
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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 | 0.000 | 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 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".