Microbial transformation of arsenic in Bengal floodplain
Bibliographic record
Abstract
Methylation is an important biotransformation process that limits the toxicity of arsenic (As) in soil. Here we investigated how geomorphology and paddy management influences As speciation, and the abundance and diversity of the arsM gene responsible for bacterial methylation of arsenic. Soil samples collected from paddy and non-paddy fields of Holocene and Pleistocene regions of Bangladesh were incubated under anaerobic conditions to identify how these treatments affected As speciation in soil solution as well as to investigate the changes in relative arsM copy no and diversity in soil. The Holocene soil had higher concentration of soil solution arsenic species (inorganic arsenic, dimethylarsinic acid (DMA), trimethylarsenic oxide (TMAO), with qPCR showing higher copy numbers of both 16S and arsM in Holocene soil compared to Pleistocene soil. Lower soil Eh may explain the higher arsM copy number in Holocene soil, with arsenic methylation known to be increased under anaerobic conditions. The higher pH in Holocene soil may also explain the increase in 16S copy number, with bacteria known to be less abundant in acidic soils. Further to that amplicon sequencing showed an increased species richness (chao1) and diversity (Simpson) for both 16S and arsM in Holocene compare to Pleistocene soil. PiCrust analysis of the 16S amplicon results showed the presence of arsenic metabolism related genes some of which were increased in Holocene compared to Pleistocene soil. The results showed that presence of As soil chemistry strongly correlates with arsenic transformation and copy number of arsenic metabolizing genes and bacterial as well as arsM gene diversity.
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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 | 0.001 |
| 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".