Potential use of endophytic root bacteria and host plants to degrade hydrocarbons
Bibliographic record
Abstract
Microbe-assisted phytoremediation depends on competent root-associated microorganisms that enhance remediation efficiency of organic compounds. Endophytic bacteria are a key element of the root microbiome and may assist plant degradation of contaminants. The aim of this study was to investigate the application of four hydrocarbon-degrading endophytic strains previously isolated from an oil sands reclamation area. Strains EA1-17 (Stenotrophomonas sp.), EA2-30 (Flavobacterium sp.), EA4-40 (Pantoea sp.), and EA6-5 (Pseudomonas sp.) were inoculated in white sweet clover growing on soils amended with diesel at 5,000, 10,000, and 20,000 mg·kg−1. Our results indicate that plant growth inhibition caused by diesel fuel toxicity was overcome in inoculated plants, which showed significantly higher plant biomass. Analysis of soil F2 and F3 hydrocarbon fractions also revealed that these soils were remediated by inoculated plants when diesel was applied at 10,000 mg·kg−1 and 20,000 mg·kg−1. In addition, quantification of hydrocarbon-degrading genes suggests that all bacterial strains successfully colonized sweet clover plants. Overall, the endophytic strain EA6-5 (Pseudomonas sp.), which harbored hydrocarbon-degrading genes, was the most effective candidate in phytoremediation experiments and could be a strategy to increase plant tolerance and hydrocarbon degradation in contaminated (e.g., diesel fuel) soils.
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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 | 0.001 | 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".