Physical and Biological Treatment of Oil-Contaminated Soil in a Baffled Roller Bioreactor
Bibliographic record
Abstract
Physical and biological removal of diesel oil from contaminated soil was studied in a baffled roller bioreactor. Initially, the effects of four factors (soil loading, temperature, pH, and surfactant) on physical removal of diesel oil were investigated. Only the presence of a surfactant (sodium dodecyl sulfate [SDS]) demonstrated a significant effect on diesel oil removal. Diesel oil removal efficiency was increased from 32.0% to 63.9% in the presence of 100 mg/L SDS. Using a microbial culture enriched from contaminated soil, biological treatment of diesel oil polluted soil under different soil loadings (15% to 50%), different diesel oil concentrations (1 to 50 g/L), and different types of soil (sand, silt, and clay) was then investigated in the baffled roller bioreactor. Biodegradation consisted of both fast and slow stages for degradation of light and heavy compounds, respectively. All biodegradation experiments demonstrated significant decreases in diesel oil concentrations (88.3% in 14 days for initial diesel oil concentrations of 1000 mg/L and a wide range of soil loadings). The presence of silty or sandy soils enhanced the biodegradation rate compared to the control bioreactor (without soil). The sandy soil loading had no effect on the biodegradation results. Using the enriched culture, the baffled roller bioreactor was able to biodegrade high diesel concentrations (up to 50 g/L) with biodegradation rates of 112.2 and 39.3 mg/L· h during fast and slow stages, respectively.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".