Spatial and Temporal Variation in Natural Source Zone Depletion Rates at a Former Oil Refinery
Bibliographic record
Abstract
Core Ideas Natural source zone depletion rate spatial variability aligns with historical release areas. The sitewide average NSZD rate is adequately characterized with radiocarbon correction. NSZD rate temporal variability correlates with air temperature and precipitation. Dynamic closed chambers, concentration gradient method, and CO 2 traps show discrepancies. Discrepancies in methods are related to parameter sensitivity, measurement time frame, and radiocarbon sampling. Understanding the implications of vadose zone processes across spatial and temporal scales is challenging. At petroleum release sites, biodegradation of hydrocarbon compounds contributes to biogeochemical cycling through natural source zone depletion (NSZD). Considerable gaps remain in characterization at large sites. An evaluation of NSZD rates at a >80‐ha decommissioned oil refinery was conducted using a dynamic closed chamber (DCC). Across the site, we characterized spatial variability and compared radiocarbon ( 14 C) and background‐corrected NSZD rate estimates. At a high‐resolution (0.5‐ha) area, temporal variability in NSZD rate estimates was characterized and a method comparison was conducted for DCC, the concentration gradient method (CGM), and CO 2 traps. The spatial variability of sitewide NSZD rates was significantly different from smear zone thickness, suggesting that additonal factors affect NSZD variability at a sitewide scale. The estimated sitewide average NSZD rate using the location‐specific 14 C correction was significantly different from the average by the background correction method (1.2 vs. 3.0 μmol CO 2 m −2 s −1 , respectively). In the high‐resolution area, NSZD rates estimated by DCC varied temporally across seasonal and daily time scales. Higher temperatures were correlated with increased NSZD, and precipitation was important in dampening effluxes following rain events. The method comparison identified high sensitivity of the CGM vs. the other methods to input parameters, while CO 2 traps showed relatively high intersample variability. Possible limitations to DCC, including measurement timeframe and 14 C sampling method, were considered. The findings assist in setting NSZD expectations at other large sites.
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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 | 0.000 |
| 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) | 0.001 | 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".