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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".