Concurrent stimulation of diflufenican biodegradation and changes in the active microbiome in gravel revealed by Total RNA
Bibliographic record
Abstract
Abstract The use of slowly degrading pesticides poses a particular problem when these are applied to urban areas such as gravel paths. The urban gravel provides an environment very different from agricultural soils; i.e., it is both lower in carbon and microbial activity. We, therefore, endeavoured to stimulate the degradation of the pesticide diflufenican added to an urban gravel microcosm amended with dry alfalfa to increase microbial activity. In the present study, the formation of the primary diflufenican metabolite 2-[3-(Trifluoromethyl)phenoxy]nicotinic acid (commonly abbreviated as AE-B) was stimulated by the alfalfa amendment. The concurrent changes of the active microbial communities within the gravel were explored using shotgun metatranscriptomic sequencing of ribosomal RNA and messenger RNA. Our results showed, that while the active microbial communities in the gravel were dominated by bacteria with a relative abundance of 87.0 – 98.5 %, the eukaryotic groups, fungi and micro-eukaryotes, both had a 4-5 fold increase in relative abundance over time in the alfalfa amended treatment. Specifically, the relative abundance of microorganisms involved in degradation of complex carbon sources, Bacteroidetes, Verrucomicrobia, Sordariomycetes, Mortierellales, and Tremellales, were shown to increase in the alfalfa amended treatment. Further, the functional gene profile showed an increase in genes involved in increased activity and production of new biomass in the alfalfa treatment compared to the control, as well as pointing to genes potentially involved in biodegradation of complex carbon sources and the biotransformation of diflufenican.
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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) | 0.000 | 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) | 0.001 | 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".