Biodegradation of Residual Dense Nonaqueous-Phase Liquid Tetrachloroethene: Effects on Mass Transfer
Bibliographic record
Abstract
The mass emissions rate of contaminants from nonaqueous-phase liquids (NAPLs) is a driving factor in remediation efforts, whether those efforts are designed to remove, transform, or stabilize the entrapped NAPL or down-gradient aqueous concentrations. Enhancement of mass flux from NAPL source zones has been previously reported in the presence of microbial reductive dechlorination activity in systems containing NAPL with a low proportion of tetrachloroethene (PCE) or a low residual saturation (e.g., 2%). The results reported here demonstrate reductive dechlorination of PCE at residual saturations of 35%, obtained under two different column flow velocities and NAPL configurations. Mass flux in biotic columns was approximately 45% greater than that in uninoculated columns, due to both the presence of daughter products and higher concentrations of PCE in the effluent from biotic columns. Daughter product concentrations were greater in columns with NAPL emplaced only in the lower quarter compared to those with NAPL throughout, and in columns run at the slower velocity. The elevated PCE concentrations in biotic column effluents suggest the influence of microbially generated surfactants, which was supported by surface tension measurements. These results demonstrate the potential significance of bioactivity within NAPL source zones on NAPL longevity and down-gradient aqueous concentrations.
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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 | 0.000 |
| 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) | 0.001 | 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".