Characterizing Vadose Zone Hydrocarbon Biodegradation Using Carbon Dioxide Effluxes, Isotopes, and Reactive Transport Modeling
Bibliographic record
Abstract
Naturally occurring biodegradation of hydrocarbon compounds may offer a sustainable management option at contaminated sites. However, a sound understanding of contaminant mass loss rates is required to enable estimation of source zone longevity, serving to alleviate public concerns and inform decision makers. Under some conditions, surficial CO 2 efflux measurements can be useful to delineate petroleum hydrocarbon containing source zones, and to provide estimates of depth‐integrated vadose zone hydrocarbon degradation rates. However, the accuracy of degradation rate estimates is limited by our ability to separate CO 2 effluxes associated with contaminant decomposition from those attributable to naturally occurring soil respiration. To understand CO 2 sources and transport processes within the vadose zone, this work combines measurement of surficial CO 2 effluxes with detailed analysis of soil gas composition– including the radiocarbon and stable isotopic composition of CO 2 . Quantitative reactive transport modeling allows further evaluation of controls on CO 2 generation and fate, and CH 4 generation and oxidation. Results confirm that, in the source zone at the Bemidji site, the majority of CO 2 originates from degradation of the oil body. In addition, radiocarbon in CO 2 proves particularly useful in determining the contribution of contaminant degradation to the measured CO 2 efflux.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".