The potential of soil microorganisms to mineralize atrazine as predicted by MCH-PCR followed by nested PCR
Bibliographic record
Abstract
The potential of soil microorganisms to mineralize atrazine was studied in soil samples collected from fields with various histories of atrazine application. In contrast to many previous studies, which showed no atrazine mineralization activity, all the tested soils mineralized atrazine regardless of their atrazine application history. However, the delay before mineralization and the variation in the subsequent mineralization rate were in agreement with the initial copy number of the atrazine dechlorinaze gene, and the proliferation rate of the degraders. Soils from corn fields, which had up to 100 copies of the atzA gene per gram of soil, had a lag period of 4-5 days before atrazine mineralization started, and final mineralization percentages ranged from 40% to 54%. However, soils from fields that were never amended with atrazine had much longer lag periods (more than 17 days), which decreased after enrichment of the degrader population with high concentrations of atrazine for 15 days. Generally the mineralization rate and the atzA gene copy number increased after the enrichment period. The atrazine mineralization potential was measured by PCR of genes from the atrazine mineralization pathway. Magnetic capture hybridization was the most efficient of the two tested methods for purifying target DNA of PCR inhibitors, without reducing the copy number of the required fragment. Nested PCR proved to be the most effective method for predicting the exact potential of the soil to mineralize the pollutant even without enrichment of a small population with the target genes. This method can complement microcosm studies and eliminate futile efforts when the potential to mineralize the pollutant does not exist in the soil.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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; 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".