Diversity and arsenic-tolerance potential of bacterial communities from soil and sediments along a gold tailing contamination gradient
Bibliographic record
Abstract
Gold tailings often release arsenic (As) contaminants into the surrounding environment. Microorganisms play an important role in the As cycle, whereas the effects of As on bacterial communities remain unclear. To reveal the effects of As on the diversity of bacterial communities and their As-tolerance potential, farmland soil and river sediment samples were collected at various distances from tailings in the Dandong area of northeastern China. The bacterial communities were analyzed using high-throughput sequencing of 16S rRNA genes. The membrane transport proteins ArsB and (or) ACR3 pump As(III) out of the cell to resist As toxicity. We studied the abundance and phylogeny of ArsB and ACR3 using PCR-based clone libraries and quantitative PCR. The bacterial community was divided into 10 phyla and 59 genera. The transformation from As(V) to As(III) was predominant, which was coupled with denitrification. Both ArsB and ACR3 likely evolved from different orders of Proteobacteria. The arsB gene seems to be more stable in bestowing bacteria with the capability to respond to the As concentration. Moreover, As with iron, manganese, and total organic carbon also influenced the clustering relationships of samples and bacterial distribution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".