Investigation of the Vertical Infiltration of Spilled Oil in Soil Impacted by Root Netting and Surface Rainfall
Bibliographic record
Abstract
Inland oil spill accidents pose a negative impact on the health of the soil ecological system and human beings. The oil infiltration process is the main behavior of spilled oil, and its infiltration is influenced by many environmental factors like root netting and rainfall. This study was conducted to investigate the impact of these two important factors on the infiltration process and reduce the pollution of inland oil spill accidents. For root nettings, they can change the soil permeability and pore volume distribution, which are important in liquid infiltration, while rainwater can change the soil water content, thereby affecting the pressure, capillary force, and buoyancy force of spilled oil in the infiltration process. In the present study, these two unique factors of oil infiltration were investigated by detecting the infiltration front head and concentration distribution of spilled oil in soil layers with various root netting structures as well as the rainfall modes. It is found that root netting and surface rainfall critically affect oil infiltration in soil. The nettings with a finer mesh size and denser plant roots show a more significant effect on the infiltration process, particularly when their pore size is smaller than that of the soil. The netting’s position and soil particle size also play crucial roles, influencing where oil accumulates within soil layers. Rainfall timing and intensity further modify oil movement, with prior rainfall reducing infiltration, while subsequent rain can increase it. The findings can help better understand the transport of spilled oil transport and implement emergency response measures for inland oil spills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".