Influence of Water Table Fluctuation on Natural Source Zone Depletion in Hydrocarbon Contaminated Subsurface Environments
Bibliographic record
Abstract
Most of the prediction theories regarding dissolution of organic contaminants in the subsurface systems have been proposed based on the static water conditions; and the influence of water fluctuations on mass removal requires further investigations. In this study, it was intended to investigate the effects of water table fluctuations on biogeochemical properties of the contaminated soil at the smear zone between the vadose zone and the groundwater table. An automated 60 cm soil column system was developed and connected to a hydrostatic equilibrium reservoir to impose the water regime by using a multi-channel pump. Four homogenized hydrocarbon contaminated soil columns were constructed and two of them were fully saturated and remained under static water conditions while another two columns were operated under water table fluctuations between the soil surface and 40 cm below it. The experiments were run for 150 days and relevant geochemical indicators as well as dissolved phase concentrations were analyzed at 30 and 50 cm below the soil surface in all columns. The results indicated significant difference in terms of biodegradation effectiveness between the smear zones exposed to static and water table fluctuation conditions. This presentation will provide an overview of the experimental approach, mass removal efficiency, and key findings.
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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 | 0.000 |
| 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 | 0.000 |
| 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".