Multiple Lines of Evidence for Estimating NSZD Rates Overlying a Shallow LNAPL Source Zone
Bibliographic record
Abstract
Abstract Quantitative methods of monitoring natural source zone depletion (NSZD) via biodegradation of petroleum hydrocarbons (PHC) are required to investigate source zone longevity and guide long‐term management of PHC impacted sites. Vadose zone NSZD processes can be monitored using analysis of surficial CO 2 effluxes, soil‐gas gradients, and thermal gradients. This study describes an applied research and development program conducted at a former refinery site over a 4‐year period (2015 to 2019) on quantitative technologies for evaluation of NSZD of PHC light nonaqueous phase liquid (LNAPL) present within a shallow soil zone. A multiyear study using discrete CO 2 efflux measurements from dynamic closed chambers was compared with estimates obtained using static traps and continuous monitoring using forced diffusion (FD) technology. Thermistor strings along a transect were used to monitor hourly thermal gradients and assess NSZD rates using the temperature gradient method. Discrete soil‐gas data were used to quantify the vertical oxygen gradient to estimate NSZD rates using the concentration gradient method (CGM). Results of discrete and continuous monitoring methods provide estimates of monthly NSZD rates that range seasonally from 80 to 1300 US gal/acre/year using radiocarbon corrected CO 2 efflux method for decane (C 10 H 22 ) equivalent, 120 to 1600 US gal/acre/year using CGM (for wet to dry conditions) and 400 to 2000 US gal/acre/year using the temperature gradient method. Both seasonal temperature and precipitation fluctuations contribute to variability in rates. Continuous methods are shown to provide for improved resolution of temporal variability and seasonal estimates, although discrete methods provide for improved spatial quantification.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".