Bacterial Reduction of Selenium in Coal Mine Tailings Pond Sediment
Why this work is in the frame
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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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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 | 0.003 | 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 it