Arsenic-hypertolerant and arsenic-reducing bacteria isolated from wells in Tucumán, Argentina
Bibliographic record
Abstract
Arsenic-hypertolerant bacteria were isolated from arsenic-contaminated well water from the village of Los Pereyra in Tucumán province, Argentina. Microorganisms that biotransform arsenic are a major factor in arsenic mobilization in contaminated aquifers. Groundwater analyses showed a level of arsenic contamination (mean concentration of 978 μg·L−1) that exceeds the safe drinking water limit of 10 μg·L−1 recommended by the World Health Organization and the Argentine Food Code. There was considerable spatial variability in the concentration of arsenic in each of the wells analyzed and in the distribution of the major anions HCO3–, SO42–, and Cl–. Eighteen bacterial strains were characterized. Six strains belonging to the Actinobacteria phylum were able to grow in media with 20 mmol·L–1 As(III) or 200 mmol·L–1 As(V) and were also highly resistant to Cr, Cd, and Cu. Their ability to biotransform arsenic was examined by speciation of the products by high-performance liquid chromatography inductively coupled plasma mass spectrometry. In addition, two strains, Brevibacterium sp. strain AE038-4 and Microbacterium sp. strain AE038-20, were capable of aerobic arsenate reduction, which suggests that these strains could increase the mobility of arsenic by formation of more mobile As(III).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".