Biodegradation of the herbicide trifluralin by bacteria isolated from soil
Bibliographic record
Abstract
Trifluralin (alpha,alpha,alpha-trifluoro-2,6-dinitro-N,N-dipropyl-p-toluidine; TFL) is a pre-emergence, soil-incorporated herbicide that has been in agricultural use since the early 1960s and is moderately persistent in soil. The purpose of this study was to isolate and characterise TFL-resistant bacteria from a soil in which this pesticide has been used for the last four decades and to determine their ability to degrade TFL using HPLC. Eight bacteria were isolated by repeated subculture in liquid medium with TFL as carbon source and a ninth (isolate 9) from growth around TFL crystals on solid medium. The bacteria from enriched liquid culture were identified by biochemical tests and 16S rDNA sequencing. In a mineral salts medium with 0.1% succinate, 0.1% yeast extract and 50 mg l(-1) TFL, reductions in the level of pesticide of 24.6% for Klebsiella sp., 16.4% for Herbaspirillum sp., 25.0% and 16.0% for two strains of Bacillus sp. and 21.0% for unidentified isolate number 9 were obtained after 30 days. These were similar to the level obtained using a known TFL-degrading bacterium, Brevundimonas diminuta (NCIMB 10329). Three Pseudomonas sp. and one Bacillus sp. reduced levels by less than 5%. The five positive isolates can be used to study the biochemical and molecular biology of TFL biodegradation with the aim of optimising the degradative ability of one or more of the isolates for future use in bioremediation processes.
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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".