Subsurface Cycling of Nitrogen and Anaerobic Aromatic Hydrocarbon Biodegradation Revealed by Nucleic Acid and Metabolic Biomarkers
Bibliographic record
Abstract
Microbial processes are crucial for ecosystem maintenance, yet documentation of these processes in complex open field sites is challenging. Here we used a multidisciplinary strategy (site geochemistry, laboratory biodegradation assays, and field extraction of molecular biomarkers) to deduce an ongoing linkage between aromatic hydrocarbon biodegradation and nitrogen cycling in a contaminated subsurface site. Three site wells were monitored over a 10-month period, which revealed fluctuating concentrations of nitrate, ammonia, sulfate, sulfide, methane, and other constituents. Biodegradation assays performed under multiple redox conditions indicated that naphthalene metabolism was favored under aerobic conditions. To explore in situ field processes, we measured metabolites of anaerobic naphthalene metabolism and expressed mRNA transcripts selected to document aerobic and anaerobic microbial transformations of ammonia, nitrate, and methylated aromatic contaminants. Gas chromatography-mass spectrometry detection of two carboxylated naphthalene metabolites and transcribed benzylsuccinate synthase, cytochrome c nitrite reductase, and ammonia monooxygenase genes indicated that anaerobic metabolism of aromatic compounds and both dissimilatory nitrate reduction to ammonia (DNRA) and nitrification occurred in situ. These data link formation (via DNRA) and destruction (via nitrification) of ammonia to in situ cycling of nitrogen in this subsurface habitat, where metabolism of aromatic pollutants has led to accumulation of reduced metabolic end products (e.g., ammonia and methane).
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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 | 0.001 |
| 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".