Pollution induced community tolerance (PICT) and analysis of 16S rRNA genes to evaluate the long‐term effects of herbicides on methanotrophic communities in soil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary There is an increasing interest in agricultural systems in which the use of herbicides is forbidden. Therefore, soils treated with herbicides atrazine and metolachlor for the last 20 years were compared with soil samples from the same field that had never been treated (control soil). We determined the pollution induced community tolerance (PICT) by evaluating the methane oxidation capacity of soil samples after adding increasing amounts of a methane oxidation inhibitor, 2,4‐dichlorophenoxyacetic acid (2,4‐D). Denaturing gradient gel electrophoresis (DGGE) of 16S rRNA genes assessed whether the soil methanotrophic community differed between the two treatments. Addition of 60 µg 2,4‐D per g soil clearly inhibited methane oxidation in both soils but increased the time needed to oxidize 5% methane in the headspace by 250% for the control soil compared with 175% for the herbicide‐treated soil. This indicates that the soil with a long‐term herbicide history had a greater tolerance to the methane oxidation inhibitor than did the control soil. The DGGE of 16S rRNA genes amplified directly from soil community DNA could also distinguish the two treatments. The banding patterns of the Type I methanotrophs contained fewer bands in the herbicide‐treated soil. It seems that both the PICT approach and DGGE analysis are effective assays to distinguish a long‐term herbicide‐treated soil from an untreated soil.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it