Arsenic and Antimony Biomethylation by<i>Scopulariopsis brevicaulis</i>: Interaction of Arsenic and Antimony Compounds
Bibliographic record
Abstract
The biomethylation of arsenic by the filamentous fungus Scopulariopsis brevicaulis is well documented, and the biomethylation of antimony by this fungus was recently established. However, in all the previous studies each metalloid was studied in isolation. Arsenic and antimony are often associated in the environment, and so an understanding of interactions between these elements is necessary. To this end, S. brevicaulis was grown in media containing mixtures of arsenic and antimony compounds in various proportions, and the principle nonvolatile biomethylation products (trimethylantimony and trimethylarsenic species) in the medium were quantified by using HG-GC-AAS. It was found that the yield of trimethylantimony compounds, obtained from the biomethylation of potassium antimony tartrate, was increased in the presence of sodium arsenite. The production of trimethylarsenic species from sodium arsenite was significantly inhibited in the presence of antimony (either as potassium antimony tartrate or antimony trioxide) at antimony concentrations too low to inhibit growth. This is although arsenic(III), in the absence of antimony, is much more readily biomethylated. That is 1.2−5.3% of added arsenic is biomethylated by S. brevicaulis whereas only 0.0006−0.008% of added antimony(III) is biomethylated over 1 month. Potassium hexahydroxyantimonate had no effect on arsenic biomethylation. The addition of potassium tartrate to cultures did not inhibit arsenic biomethylation. The biomethylation of sodium arsenate was not inhibited as much by antimony compounds. The inhibitory effect of antimony was found to be a function of the ratio of antimony to arsenic rather than the absolute amount of antimony.
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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) | 0.001 | 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) | 0.001 | 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".