Degradation of p -nitrophenol and pentachlorophenol mixtures by Sphingomonas sp. UG30 in soil perfusion bioreactors
Bibliographic record
Abstract
The degradation of mixtures of pentachlorophenol (PCP) and p-nitrophenol (PNP) were evaluated in pure cultures of Sphingomonas sp. UG30, statically incubated soils (60% water-holding capacity) and soil perfusion bioreactors where encapsulated cells of UG30 were used as a soil inoculant. In pure-culture studies, conditions were optimized for mineralization of PCP and PNP mixtures at concentrations of 30 mg l−1 each. Optimum in vitro mineralization of PCP and PNP mixtures by UG30 was facilitated using ammonium phosphate as a nitrogen source, while inhibition was observed with ammonium nitrate. The bioreactor system used columns containing soil treated with mixtures of 100, 225 or 500 mg kg−1 of PCP and PNP. Rapid dissipation of both substrates was observed at the 100 mg kg−1 level. Inoculation with UG30 enhanced PCP degradation at the 100 mg kg−1 level in bioreactors but not in static soil microcosms. At higher PCP and PNP concentrations (225 mg kg−1), occasional complete degradation of PNP was observed, and PCP degradation was about 80% compared to about 25% in statically incubated soils after 20 days at 22°C. There was no additional degradation of the PCP and PNP mixtures attributable to inoculation with encapsulated cells of UG30 in either soil system at concentrations of 225 or 500 mg kg−1. Journal of Industrial Microbiology & Biotechnology (2000) 25, 93–99.
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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) | 0.001 | 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".