Migration Behavior Of Light Non Aqueous Phase Liquid (Lnapl) At Unsaturated Zone Due To Precipitation And Fluctuating Groundwater Table
Bibliographic record
Abstract
Oil spill from petroleum products in the underground can be very problematic. Initially, it is released as LNAPL in the form of gasoline, diesel fuel, lube oils and other petroleum products. It causes contamination to the soil. As a result, remedial work has to be done to retrieve the oil spill to clear the contaminated soil and groundwater. Thus, the oil spill pattern has to be predicted. The purpose of the study was to conduct an experimental model to find out the effect of precipitation and fluctuating groundwater table towards the migration behaviour of LNAPL. SIAM was adopted to access the saturation distribution of LNAPL and water in the 2D water tank. The precipitation was simulated by a valve and peristaltic pump to provide a constant rainfall rate of 8mm/hr throughout the one hour of stipulated time. The result was then analysed and assessed by using MATLAB and Microsoft Excel to get a more thorough understanding about the data collected. From the experiment, it can be concluded that the saturation of soil will increase an average of 36.9% after one hour of 8mm/hr precipitation. The presence rainfall after the LNAPL has been stabilised at the water table will cause the LNAPL to migrate deeper into the soil layer. When the water table increases from 28cm to 38cm, the LNAPL will be pushed upwards, causes an increase of LNAPL saturation in the unsaturated zone. Then, when the water table returns to normal, most of the LNAPL saturation will decrease. However, part of the LNAPL pool remains in the unsaturated zone causes a smear zone. In conclusion, SIAM shows the migration behaviour of LNAPL and water in a two-dimensional tank effectively.
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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) | 0.001 | 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".