Nutrient Stimulation of Sulfolane Biodegradation in a Contaminated Soil from a Sour Natural Gas Plant and in a Pristine Soil
Bibliographic record
Abstract
Natural gas in western Canada can contain up to 35% H2S. The Sulfinol process for sour gas treatment makes use of sulfolane and an amine to remove H2S and other sour components from natural gas. Sulfolane has leached into groundwaters at sour gas treatment plant sites, and poses a risk for off-site contamination. Sulfolane biodegradation was monitored in shake-flask cultures and air-sparged microcosms inoculated with uncontaminated topsoil or with sulfolane contaminated soil obtained near a Sulfinol process building at a sour gas treatment facility in western Canada. For both soils, supplementation with a source of fixed nitrogen stimulated sulfolane biodegradation. Topsoil cultures and microcosms were only slightly affected by the addition of phosphate. Contaminated soil microcosms and cultures were stimulated by phosphate addition, but not to the same degree as by the addition of nitrogen. For these cultures and microcosms, amendment with both fixed nitrogen and phosphate produced an additive effect. It was possible to predict the nutrient requirements of air-sparged microcosms inoculated with each soil type using shake-flask cultures. Shake-flask cultures require less time and effort and fewer materials than the more complex air-sparged soil microcosms, and will be useful for large-scale experiments to predict the nutrient supplements required for bioremediation of sulfolane-contaminated sites.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".