Assessment of 2,4,6-trinitrotoluene toxicity in field soils by pollution-induced community tolerance, denaturing gradient gel electrophoresis, and seed germination assay
Bibliographic record
Abstract
Abstract Determining the toxicity of contaminants to soil organisms under field conditions is hampered by site-specific and temporal factors that modulate contaminant availability. Assessing the pollution-induced community tolerance (PICT) of indigenous microbial communities integrates these complex environmental factors. The purpose of this study was to determine if the PICT response was proportional to 2,4,6-trinitrotoluene (TNT) concentrations in soil, if changes detected by PICT were also evident in soil microbial community composition, and if the PICT response correlated with phytotoxicity assays. Microorganisms extracted from TNT-contaminated field soils were mixed with a solution containing six different concentrations of TNT and inoculated into Biolog ECO plates. The utilization rate of substrates was determined over a 7-d period. Denaturing gradient gel electrophoretic analysis of a portion of the gene encoding 16S rDNA described the structure of the soil microbial community. Phytoindicators (Poa compressa and P. palustris L.) of TNT pollution were identified and used to assess TNT phytotoxicity in soil samples. The TNT (in Biolog wells) greatly inhibited microbial communities from locations with low in situ TNT exposure. The inhibition of microbial use of L-asparagine, L-phenylalanine, and D-glucosaminic acid by TNT (in Biolog wells) increased as TNT concentration in soil decreased. Locations differing in ECO-PICT response also differed in their microbial community composition and TNT phytotoxicity. Decreased phytotoxicity of field soils corresponded to decreases in PICT. The results from this study indicated that ECO-PICT is an effective assay to rapidly detect TNT exposure and toxicity in soil microbial communities.
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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".