Combined Use of Diagnostic Fumarate Addition Metabolites and Genes Provides Evidence for Anaerobic Hydrocarbon Biodegradation in Contaminated Groundwater
Bibliographic record
Abstract
The widespread use of hydrocarbon-based fuels has led to the contamination of many natural environments due to accidental spills or leaks. While anaerobic microorganisms indigenous to many fuel-contaminated groundwater sites can play a role in site remediation (e.g., monitored natural attenuation, MNA) via hydrocarbon biodegradation, multiple lines of evidence in support of such bioremediation are required. In this study, we investigated two fuel-contaminated groundwater sites for their potential to be managed by MNA. Microbial community composition, biogeochemical indicators, fumarate addition metabolites, and genes diagnostic of both alkane and alkyl-monoaromatic hydrocarbon activation were assessed. Fumarate addition metabolites and catabolic genes were detected for both classes of hydrocarbon biodegradation at both sites, providing strong evidence for in situ anaerobic hydrocarbon biodegradation. However, relevant metabolites and genes did not consistently co-occur within all groundwater samples. Using newly designed mixtures of quantitative polymerase chain reaction (qPCR) primers to target diverse assA and bssA genes, we measured assA gene abundances ranging from 105–108 copies/L, and bssA gene abundances ranging from 105–1010 copies/L at the sites. Overall, this study demonstrates the value of investigating fuel-contaminated sites using both metabolites and genes diagnostic of anaerobic hydrocarbon biodegradation for different classes of hydrocarbons to help assess field sites for management by MNA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".