Production and Release of Selenomethionine and Related Organic Selenium Species by Microorganisms in Natural and Industrial Waters
Bibliographic record
Abstract
Laboratory algal cultures exposed to selenate were shown to produce and release selenomethionine, selenomethionine oxide, and several other organic selenium metabolites. Released discrete organic selenium species accounted for 1.6-13.1% of the selenium remaining in the media after culture death, with 1.3-6.1% of the added selenate recovered as organic metabolites. Analysis of water from an industrially impacted river collected immediately after the death of massive annual algal blooms showed that no selenomethionine or selenomethionine oxide was present. However, other discrete organic selenium species, including a cyclic oxidation product of selenomethionine, were observed, indicating the previous presence of selenomethionine. Industrial biological treatment systems designed for remediation of selenium-contaminated waters were shown to increase both the concentration of organic selenium species in the effluent, relative to influent water, and the fraction of organic selenium to up to 8.7% of the total selenium in the effluent, from less than 1.1% in the influent. Production and emission of selenomethionine, selenomethionine oxide, and other discrete organic selenium species were observed. These findings are discussed in the context of potentially increased selenium bioavailability caused by microbial activity in aquatic environments and biological treatment systems, despite overall reductions in total selenium concentration.
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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 | 0.001 |
| 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 | 0.001 |
| 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".