Predicting Vertical <scp>LNAPL</scp> Distribution in the Subsurface under the Fluctuating Water Table Effect
Bibliographic record
Abstract
Abstract The present study proposes a methodology for predicting the vertical light nonaqueous‐phase liquids (LNAPLs) distribution within an aquifer by considering the influence of water table fluctuations. The LNAPL distribution is predicted by combining (1) information on air/LNAPL and LNAPL/water interface elevations with (2) the initial elevation of the water table without LNAPL effect. Data used in the present study were collected during groundwater monitoring undertaken over a period of 4 months at a LNAPL‐impacted observation well. In this study, the water table fluctuations raised the free LNAPL in the subsurface to an elevation of 206.63 m, while the lowest elevation was 205.70 m, forming a thickness of 0.93 m of LNAPL‐impacted soil. Results show that the apparent LNAPL thickness in the observation well is found to be three times greater than the actual free LNAPL thickness in soil; a finding that agrees with previous studies reporting that apparent LNAPL thickness in observation wells typically exceeds the free LNAPL thickness within soil by a factor estimated to range between 2 and 10. The present study provides insights concerning the transient variation of LNAPL distribution within the subsurface and highlights the capability of the proposed methodology to mathematically predict the actual LNAPL thickness in the subsurface, without the need to conduct laborious field tests. Practitioners can use the proposed methodology to determine by how much the water table should be lowered, through pumping, to isolate the LNAPL‐impacted soil within the unsaturated zone, which can then be subjected to in situ vadose zone remedial treatment.
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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 | 0.000 |
| 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".