Optimization of nitrogen for bioventing of gasoline contaminated soil
Bibliographic record
Abstract
Bioventing is a promising in situ remediation technology for hydrocarbon contaminated soil. Using low airflow rates to produce oxygen-rich conditions in the vadose zone, and nutrient addition, bioventing stimulates indigenous microorganisms that degrade the hydrocarbon contaminants. However, several questions about bioventing remain to be answered, including the optimum soil water content, type and amount of nutrients necessary, and contributions of different microbes. Experiments were conducted using small-scale respirometers containing gasoline-contaminated soil from an active remediation site to determine the effects of soil water content, nitrogen content, nitrogen form, and the composition of the microbial population on the gasoline biodegradation rate. Results indicate that optimum bioventing conditions were 18 wt.% soil water content, C:N = 10:1, using NH4+-N. A maximum first-order degradation rate constant of 0.12/d was observed. Biodegradation was limited at high C:N ratios by the availability of nitrogen and at low C:N ratios by acidification. It was also determined that aerobic bacteria were the dominant group responsible for biodegradation, with fungi playing a minor role. Key words: bioventing, degradation rate, nutrients, water content, scale-up, gasoline, microbial population.
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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 | 0.001 |
| 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".