Reduction in denitrification activity in field soils exposed to long term contamination by 2,4,6-trinitrotoluene (TNT)
Bibliographic record
Abstract
Terrestrial sites contaminated with 2,4,6-trinitrotoluene (TNT) are a widespread and persistent problem and often contain non-vegetated areas with TNT concentrations well in excess of 1000 mg kg(-1). In this study, we examined the effect of TNT on denitrification activity in field soils, and compared the sensitivity of denitrifying enzymes to TNT. DNA probes assessed the prevalence of nirS, nirK and nosZ (encoding cd(1) or copper nitrite reductase and nitrous oxide reductase, respectively), denitrifying genotypes in the culturable and total microbial community. The nitrate (NaR), nitrite (NiR) and nitrous oxide (N(2)OR) reductase activities in field soil and in isolates were assessed by gas chromatography. The relative occurrence of the nirK, nirS or nosZ genotypes increased in the cultured community and in total uncultured community DNA as nitroaromatic concentrations increased. However, denitrifying activity decreased in response to increasing TNT concentrations, with an IC(50) for NaR+NiR+nitric oxide reductase (NOR) of 400 mg TNT kg(-1) soil and for N(2)OR of 26 mg TNT kg(-1) soil. The denitrifying activity of four soil isolates also decreased in response to TNT, with N(2)OR activity being three times more sensitive to TNT than NaR+NiR+NOR activity. Interestingly, there were 118 times more nirK isolates than nirS isolates in uncontaminated soil but only 1.5 times more in soil containing 17400 mg kg(-1) TNT. The results from this study indicated that TNT reduced denitrification activity in field soils, and N(2)OR was much more sensitive to TNT than NaR+NiR+NOR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".