Seasonal Variability in Vadose Zone Biodegradation at a Crude Oil Pipeline Rupture Site
Bibliographic record
Abstract
Core Ideas Contaminant mass loss rates vary seasonally. Vadose zone temperatures in the source zone correlate with rates of contaminant respiration. Natural soil respiration and gas transport seasonality affect mass loss estimates. Understanding seasonal changes in natural attenuation processes is critical for evaluating source‐zone longevity and informing management decisions. The seasonal variations of natural attenuation were investigated through measurements of surficial CO 2 effluxes, shallow soil CO 2 radiocarbon contents, subsurface gas concentrations, soil temperature, and volumetric water contents during a 2‐yr period. Surficial CO 2 effluxes varied seasonally, with peak values of total soil respiration (TSR) occurring in the late spring and summer. Efflux and radiocarbon data indicated that the fractional contributions of natural soil respiration (NSR) and contaminant soil respiration (CSR) to TSR varied seasonally. The NSR dominated in the spring and summer, and CSR dominated in the fall and winter. Subsurface gas concentrations also varied seasonally, with peak values of CO 2 and CH 4 occurring in the fall and winter. Vadose zone temperatures and subsurface CO 2 concentrations revealed a correlation between contaminant respiration and temperature. A time lag of 5 to 7 mo between peak subsurface CO 2 concentrations and peak surface efflux is consistent with travel‐time estimates for subsurface gas migration. Periods of frozen soils coincided with depressed surface CO 2 effluxes and elevated CO 2 concentrations, pointing to the temporary presence of an ice layer that inhibited gas transport. Quantitative reactive transport simulations demonstrated aspects of the conceptual model developed from field measurements. Overall, results indicated that source‐zone natural attenuation (SZNA) rates and gas transport processes varied seasonally and that the average annual SZNA rate estimated from periodic surface efflux measurements is 60% lower than rates determined from measurements during the summer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".