Assessment of Pollution-Induced Microbial Community Tolerance to Heavy Metals in Soil Using Ammonia-Oxidizing Bacteria and Biolog Assay
Bibliographic record
Abstract
This study attempted to investigate if the tolerance of soil bacterial communities in general, and autotrophic ammonia-oxidizing bacteria (AOB) in particular, evolved as a result of prolonged exposure to metals, and could be used as an indigenous bioindicator for soil metal pollution. A soil contaminated with copper, chromium, and arsenic (CCA) was mixed with an uncontaminated garden soil (GS3) to make five test soils with different metal concentrations. A modified potential ammonium oxidation assay was used to determine the metal tolerance of the AOB community. Tolerance to Cr, Cu, and As was tested at the beginning and after up to 13 months of incubation. Compared with the reference GS3 soil, the five CCA soils showed significantly higher tolerance to Cr no matter which form of Cr (Cr3+, CrO4 2−, or Cr2O7 2−) was tested, and the Cr tolerance correlated with the total soil Cr concentration. However, the tolerance to Cu2+, As3+, and As5+ did not differ significantly between the GS3 soil and the five CCA soils. Community level physiological profiles using Biolog microtiter plates were also used to examine the chromate tolerance of the bacterial communities extracted after six months of exposure. Our results showed that the bacterial community tolerance was altered and increased as the soil Cr concentration was increased, indicating that the culturable microbial community and the AOB community responded in a similar manner.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".