Laboratory Study of Treatment of Trichloroethene by Chemical Oxidation Followed by Bioremediation
Bibliographic record
Abstract
Studies were conducted with columns containing soil and emplaced trichloroethene (TCE) to investigate the potential for TCE source zone remediation with chemical oxidation followed by biologically mediated reductive dehalogenation. Following permanganate flushing of four columns, which resulted in rapid but incomplete removal of TCE DNAPL, no biological activity was observed following the addition of distilled water amended with ethanol and acetate, including two of the four columns that were bioaugmented with a TCE-dechlorinating microbial culture. Flushing with unsterilized site groundwater led to consumption of acetate and ethanol, accompanied by manganese reduction and methanogenesis. Reductive dechlorination of TCE to cis-1,2-dichloroethene (cis-DCE) followed the onset of ethanol and acetate biodegradation in bioaugmented columns only. Partial dechlorination of TCEto ethene was observed only in one of the bioaugmented columns after it was inoculated for a third time. At the end of the study (290 days), a trace amount of cis-DCE was observed in one of the two columns which was not bioaugmented. Reduced conditions created by biostimulation were also conducive to reduction of Mn(IV) from MnO2 in both bioaugmented and nonbioaugmented columns resulting in an increased dissolved manganese (Mn2+) concentration in groundwater.
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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) | 0.001 | 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) | 0.000 | 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".