Bibliographic record
Abstract
Arsenic is toxic to most living cells and has two soluble inorganic forms: arsenite (+3) \nand arsenate (+5), which are ubiquitous in the environment. Microbial metabolism of \narsenic in the environment, contributes to its geochemical cycling. Prokaryotic \noxidation of arsenite has been reported and characterised in moderate and thermal \nenvironments but not below 10°C. \nGiant Mine is a discontinued gold mine located 250 miles south of the arctic circle in \nthe Northwest Territories, Canada. 230,000 tonnes of arsenic trioxide dust are stored \nunderground at the site and infiltrating surface waters have become contaminated with \n>50 mM arsenic. Several microbial biofilms were found growing on the mine walls \nbeneath seepage points of the arsenic-contaminated water. The diversity of arsenite \noxidisers in two sub-samples (which differed in arsenite concentrations) of one biofilm \nwere compared using a functional gene approach. The diversity of the two sub-samples \ndid not differ but the relative abundance of the three identified clades did. \nAn arsenite-oxidising bacterium, designated GM1, was isolated from the Giant Mine \nbiofilm. GM1 was shown to be a member of the Polaromonas genus, had a growth \nrange of 0-25°C and oxidised arsenite in the early-log phase of growth. GM1’s arsenite \noxidase was constitutively expressed. The arsenite oxidase genes were partially \nsequenced and their role in arsenite oxidation confirmed by mutagenesis. \nThe arsenite oxidase of GM1 was purified and partially characterised. It consists of two \nsubunits (88 and 15 kDa) in a α1β1 conformation, and contained Mo and Fe as cofactors. \nThe Vmax, Kcat and Km were the highest of any known arsenite oxidase. The GM1 \narsenite oxidase functioned over a broad temperature range and was more active than \nthat of the mesophile NT-26 at low temperatures. It was also found to be less stable than \nthat of NT-26, as observed by circular dichroism spectroscopy.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".