Microbial community structure and trichloroethylene degradation in groundwater
Bibliographic record
Abstract
Trichloroethylene (TCE) is a prevalent contaminant of groundwater that can be cometabolically degraded by indigenous microbes. Groundwater contaminated with TCE from a US Department of Energy site in Ohio was used to characterize the site-specific impact of phenol on the indigenous bacterial community for use as a possible remedial strategy. Incubations of 14C-TCE-spiked groundwater amended with phenol showed increased TCE mineralization compared with unamended groundwater. Community structure was determined using DNA directly extracted from groundwater samples. This DNA was then analyzed by amplified ribosomal DNA restriction analysis. Unique restriction fragment length polymorphisms defined operational taxonomic units that were sequenced to determine phylogeny. DNA sequence data indicated that known TCE-degrading bacteria including relatives of Variovorax and Burkholderia were present in site water. Diversity of the amplified microbial rDNA clone library was lower in phenol-amended communities than in unamended groundwater (i.e., having Shannon-Weaver diversity indices of 2.0 and 2.2, respectively). Microbial activity was higher in phenol-amended ground water as determined by measuring the reduction of 2-(p-iodophenyl)-3(p-nitrophenyl)-5-phenyl tetrazolium chloride. Thus phenol amendments to groundwater correlated with increased TCE mineralization, a decrease in diversity of the amplified microbial rDNA clone library, and increased microbial activity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".