Linking selenium biogeochemistry to the sulfur‐dependent biological detoxification of arsenic
Bibliographic record
Abstract
Geochemistry often reveals unexpected (anti)correlations. Arsenic (As) and selenium (Se) are cases in point. We explore the hypothesis that bacteria living in an As-replete environment recruited a biological process involving Se and sulfur to fulfil their need for As detoxification. In analogy with the formation of arsenolipids and arsenosugars, which are common non-toxic As metabolites derived from microbial and plant metabolism, we attempt to explain the prevalence of novel sulfur-containing As derivatives, in particular monothioarsenate, in the aqueous environment. Thiolated-As species have been overlooked so far mainly because of the difficulty of their identification. Based on comparative genomics, we propose a scenario where SelD and SelU proteins, commonly used to make selenophosphate and modify transfer RNA, have been recruited to make monothioarsenate, a relatively innocuous arsenical. This hypothesis is discussed in terms of the relative geochemical distribution of Se and As.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".