Bibliographic record
Abstract
In general, bacteria are capable of biotransforming inorganic arsenic into methylarsenic acids and arsines. The microbial activity of lake sediments was examined with respect to the mobilization of mine tailings that have a high arsenic content. Aerobic and anaerobic mixed microbial populations were isolated from Kam Lake, Yellowknife, N.W.T. An aerobic microbial population from 5 cm sediment depth, the layer immediately above the contaminated mine tailings, was capable of transforming arsenicals. Speciation of arsenicals in the culture medium, determined by using hydride generation - gas chromatography - atomic absorption spectrometry (HG-GC-AAS) shows that this bacterial population is able to methylate arsenicals and subsequently demethylate the product. However, only methylation was observed in media containing dimethylarsinic acid. Anaerobic microbial populations, from all depths, produce a yellow precipitate upon incubation with arsenate for 10-14 days. The precipitate was identified as AS₂S₃ by microanalysis and scanning electron microscope + energy dispersive x-ray (SEM + EDX). The anaerobic microbial population, which should not contain sulfate-reducing organisms, appears to be arsenic tolerant; there is no evidence of methylation of arsenic.
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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 | 0.000 |
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".