Comparative Assessments of Benzene, Toluene, and Xylene Natural Attenuation by Quantitative Polymerase Chain Reaction Analysis of a Catabolic Gene, Signature Metabolites, and Compound-Specific Isotope Analysis
Bibliographic record
Abstract
A controlled-release study conducted at Vandenberg Air Force Base involved the injection of anaerobic groundwater amended with benzene, toluene, and o-xylene (BToX; 1-3 mg/L each) in two parallel lanes: lane A injectate contained no ethanol, whereas lane B injectate contained approximately 500 mg/L ethanol. As reported previously by Mackay and co-workers, ethanol led to slower BToX disappearance in lane B. Here, we report on assessments of BToX natural attenuation by three independent and specific monitoring approaches: signature metabolites diagnostic of anaerobic TX metabolism (benzysuccinates), compound-specific isotope analysis (CSIA), and quantitative polymerase chain reaction (qPCR) analysis of a catabolic gene involved in anaerobic TX degradation (bssA). In combination, the three monitoring methods provided strong evidence of in situ TX biodegradation in both lanes A and B; however, no single method provided strong evidence for TX biodegradation in both lanes. Benzylsuccinates were detected almost exclusively in lane B, where slower TX degradation and higher residual TX concentrations led to higher metabolite concentrations. In contrast, CSIA provided evidence of TX biodegradation almost exclusively in lane A, as greater degradation rates led to more pronounced isotopic enrichment. qPCR analyses of bssA were more complex. Evidence of increases in bssA copy number (up to 200-fold) after the release started was stronger in lane A, but higher absolute bssA copy number (and bacterial abundance, based on 16S rRNA genes) was observed in lane B, where bacteria genetically capable of anaerobic TX degradation may have been growing primarily on ethanol or its metabolites rather than TX.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".