Carbon Isotope Fractionation during Microbial Dechlorination of Trichloroethene, <i>cis</i>-1,2-Dichloroethene, and Vinyl Chloride: Implications for Assessment of Natural Attenuation
Bibliographic record
Abstract
Carbon isotope fractionation during dechlorination of chlorinated ethenes was investigated using a methanogenic microbial enrichment culture. Subcultures were amended with trichloroethene (TCE), cis -1,2-dichloroethene ( cis -DCE), and vinyl chloride (VC), respectively. Carbon isotope ratios and concentrations of reactants and of all dechlorination products were monitored during two experiments. All dechlorination steps were accompanied by significant isotope fractionation. The isotope ratios of the reactants were described with a Rayleigh type model, and the following enrichment factors (ε P/R ) were obtained: −6.6 and −2.5‰ for dechlorination of TCE, −14.1 and −16.1‰ for dechlorination of cis -DCE, and −26.6 and −21.5‰ for dechlorination of VC. Isotope and mass balances suggested that ethene (ETH) was degraded. In additional experiments with ETH as reactant, ETH became enriched in 13 C as its concentration decreased indicating the cultures were capable of degrading ETH. The average value for the enrichment factor obtained for the degradation of ETH was −3.0‰. The large carbon isotope fractionation observed in this study confirms that carbon isotope ratios are a sensitive tool for monitoring dechlorination of chlorinated ethenes to nontoxic end products.
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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) | 0.000 | 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".