Biodegradation of catechol by <scp><i>Pseudomonas fluorescens</i></scp> isolated from petroleum‐impacted soil
Bibliographic record
Abstract
Abstract Bioremediation strategies have been applied to clean up petroleum hydrocarbon (PHC) impacted sites. Introducing PHC degrading microorganisms (bioaugmentation) and enhancing the in‐situ nutrients availability (biostimulation) are widely used strategies. These strategies can be combined to lead to a better bioremediation performance. In this work, Pseudomonas fluorescens was isolated from a PHC impacted site. Through a 23 factorial design plan, the effect of various combinations of nitrate, sulphate, and phosphate ions on the PHC bioremediation performance by P. fluorescens was investigated using catechol, an essential metabolic intermediate of BTEX degradation, as the sole carbon source. The maximum specific catechol degradation rate was chosen as the response to evaluate the catechol bioremediation performance. The ANOVA results indicated that the presence of nitrate ions alone lowered the maximum specific catechol degradation rate, which can be explained by the accumulation of nitrites and ammonia during the denitrification process by P. fluorescens. It was noted that dosing sulphate ions alone did not affect the bioremediation performance, which indicates P. fluorescens can grow in a sulphur‐limited environment. In contrast, the presence of sulphate and nitrate ions together can lead to a higher specific catechol degradation rate. This may be caused by the presence of sulphate that can suppress the production of nitrites. The importance of phosphate ions on catechol biodegradation was investigated. The absence of phosphate led to incomplete biodegradation. Introducing phosphate ions can accelerate catechol degradation, which can be explained by the secretion of organic acids.
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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 | 0.001 | 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".