Production and Release of Selenocyanate by Different Green Freshwater Algae in Environmental and Laboratory Samples
Bibliographic record
Abstract
In a previous study, selenocyanate was tentatively identified as a biotransformation product when green algae were exposed to environmentally relevant concentrations of selenate. In this follow-up study, we confirm conclusively the presence of selenocyanate in Chlorella vulgaris culture medium by electrospray mass spectrometry, based on selenium's known isotopic pattern. We also demonstrate that the observed phenomenon extends to other green algae (Chlorella kesslerii and Scenedesmus obliquus) and at least one species of blue-green algae (Synechococcus leopoliensis). Further laboratory experiments show that selenocyanate production by algae is enhanced by addition of nitrate, which appears to serve as a source of cyanide produced in the algae. Ultimately, this biotransformation process was confirmed in field experiments where trace amounts of selenocyanate (0.215 ± 0.010 ppb) were observed in a eutrophic, selenium-impacted river with massive algal blooms, which consisted of filamentous green algae (Cladophora genus) and blue-green algae (Anabaena genus). Selenocyanate abundance was low despite elevated selenium concentrations, apparently due to suppression of selenate uptake by sulfate, and insufficient nitrogen concentrations. Finally, trace levels of several other unidentified selenium-containing compounds were observed in these river water samples; preliminary suggestions for their identities include thioselenate and small organic Se species.
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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".