Investigation of stable carbon compound specific isotope analysis to monitor and quantify the biodegradation of chlorinated ethenes in groundwater systems
Bibliographic record
Abstract
Identification and quantification of chlorinated ethene biodegradation were investigated using carbon compound specific isotope analysis (CSIA). A dynamic headspace sampling technique was developed and shown to be a robust, simple and effective method for isotopic analysis of dissolved chlorinated ethenes at low concentrations ([mu]g/L). This technique was used to monitor the biodegradation of tetrachloroethene (PCE) to ethene at a contaminated field site. At the field site, significant isotopic fractionation characteristic of biodegradation was observed in the isotope values of PCE and its degradation products trichloroethene (TCE), cis -1,2-dichloroethene (cDCE), and vinyl chloride (VC). During this study, stable carbon isotope analysis also provided one of the earliest lines of evidence for biodegradation. In laboratory and field experiments with an emplaced PCE dense nonaqueous phase liquid (DNAPL) source, isotopic fractionation was observed in the isotope values of the dechlorination intermediates produced by biodegradation, but not in the aqueous PCE near the DNAPL. These results confirm the hypothesis that isotopic fractionation due to biodegradation will not be observed in aqueous PCE close to the source zone. A model was developed to predict concentration values from isotope data for each step in the sequential reaction of PCE to non-toxic ethene. ...
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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".