Measurement of Natural Losses of <scp>LNAPL</scp> Using <scp> CO <sub>2</sub> </scp> Traps
Bibliographic record
Abstract
Efflux of CO2 above releases of petroleum light nonaqueous phase liquids (LNAPLs) has emerged as a critical parameter for resolving natural losses of LNAPLs and managing LNAPL sites. Current approaches for resolving CO2 efflux include gradient, flux chamber, and mass balance methods. Herein a new method for measuring CO2 efflux above LNAPL bodies, referred to as CO2 traps, is introduced. CO2 traps involve an upper and a lower solid phase sorbent elements that convert CO2 gas into solid phase carbonates. The sorbent is placed in an open vertical section of 10 cm ID polyvinyl chloride (PVC) pipe located at grade. The lower sorbent element captures CO2 released from the subsurface via diffusion and advection. The upper sorbent element prevents atmospheric CO2 from reaching the lower sorbent element. CO2 traps provide integral measurement of CO2 efflux based over the period of deployment, typically 2 to 4 weeks. Favorable attributes of CO2 traps include simplicity, generation of integral (time averaged) measurement, and a simple means of capturing CO2 for carbon isotope analysis. Results from open and closed laboratory experiments indicate that CO2 traps quantitatively capture CO2 . Results from the deployment of 23 CO2 traps at a former refinery indicate natural loss rates of LNAPL (measured in the fall, likely concurrent with high soil temperatures and consequently high degradation rates) ranging from 13,400 to 130,000 liters per hectare per year (L/Ha/year). A set of field triplicates indicates a coefficient of variation of 18% (resulting from local spatial variations and issues with measurement accuracy).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".