Enhanced Soil Decontamination via Electrokinetic Removal of Organic Pollutants
Bibliographic record
Abstract
Soil pollution is one of the concern issues in the Asia region. Soil acts as a shelter for underground microorganisms and provides nutrients for plants. Most of the organic contaminants are sourced from agriculture and industrial areas. Organic contaminants which are volatilized and immiscible lead to air and water pollution. Electrokinetic remediation is a technology that has been developed for soil remediation since a few decades ago. It is not fully developed and is still under investigation. Electrokinetic remediation is being applied to improve the removal efficiency of organic contaminants which exist in low hydraulic conductivity of soil or fine-grained soil. Generally, a low direct current, 1DCV/cm is applied. Facilitating agents including surfactant and co-solvent combined with electrokinetic remediation eliminated more organic contaminants compared with electrokinetic remediation alone. Electrokinetic remediation with the addition of bioremediation or phytoremediation process manipulates the transportation of organic contaminants in soil to increase the efficiency of remediation technologies. Electrokinetic remediation is recommended due to its flexibility, cost-effectiveness, and safety. One of the drawbacks is low effectiveness in removing non-polar organic pollutants due to weak desorption capacity and poor solubility in water. Co-solvents and surfactants can be introduced as alternatives to enhancing the solubility of non-polar pollutants and reducing surface tension, which improves their mobility within the soil matrix. These facilitating agents help improve the overall effectiveness of electrokinetic remediation, particularly for challenging contaminants.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".