Bacterial Reduction of Selenium in Coal Mine Tailings Pond Sediment
Bibliographic record
Abstract
Sediment from a storage facility for coal tailings solids was assessed for its capacity to reduce selenium (Se) by native bacterial community. One Se(6+)-reducing bacterium Enterobacter hormaechei (Tar11) and four Se(4+)-reducing bacteria, Klebsiella pneumoniae (Tar1), Pseudomonas fluorescens (Tar3), Stenotrophomonas maltophilia (Tar6), and Enterobacter amnigenus (Tar8) were isolated from the sediment. Enterobacter hormaechei removed 96% of the added Se(6+) (0.92 mg L(-1)) from the effluents when Se(6+) was determined after 5 d of incubation. Analysis of the red precipitates showed that Se(6+) reduction resulted in the formation of spherical particles (<1.0 microm) of Se(0) as observed under scanning electron microscope (SEM) and confirmed by EDAX. Selenium speciation was performed to examine the fate of the added Se(6+) in the sediment with or without addition of Enterobacter hormaechei cells. More than 99% of the added Se(6+) (approximately 2.5 mg L(-1)) was transformed in the nonsterilized sediment (without Enterobacter hormaechei cells) as well as in the sterilized (heat-killed) sediment (with Enterobacter hormaechei cells). The results of this study suggest that the lagoon sediments at the mine site harbor Se(6+)- and Se(4+)-reducing bacteria and may be important sinks for soluble Se (Se(6+) and Se(4+)). Enterobacter hormaechei isolated from metal-contaminated sediment may have potential application in removing Se from industrial effluents.
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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) | 0.000 | 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".