Removal of soluble Se from mining influenced water by native mine site bacteria depends on the consortium composition
Bibliographic record
Abstract
Abstract Soluble Se compounds are of great concern in mine influenced water (MIW) from many coal and metal mines due to Se bioaccumulation in aquatic environments and toxicity to birds and fish. Biological treatment to remove soluble Se to regulated levels, which are on the orders of µg-Se/L, is challenging due to the chemical and biological complexity of MIW. For instance, co-contaminant nitrate can inhibit selenate reduction. Native bacteria consortia from mine impacted aquatic environments are sources for known and novel selenate reducing bacteria. In this study, two consortia of native bacteria enriched from different locations on a coalmine known to exhibit elevated release of Se were tested for their ability to remove soluble Se from a typical MIW in sequencing batch bioreactors. One consortium, enriched from an impacted natural vegetated wetland known to harbour native microorganisms involved in selenate-Se reduction, when inoculated into MIW achieved limited soluble Se removal in the presence of nitrate. The other consortium enriched from a disused tailing storage facility achieved greater removal of soluble Se in the presence of nitrate. Genome-resolved metagenomics were used to identify and track consortium members and identify putative novel selenate reducing microorganisms.
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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".