Hydrocarbon-Contamination of Soils: The Potential of Sodium Clays to Decelerate Soil Toxicants
Bibliographic record
Abstract
Hydrocarbon-contamination can change hydraulic conductivity (HC) in soils, and hence increase the spreading rate of aqueous toxicants in the ground. A constant head permeameter used in the laboratory to measure HC of soils taken from near the Pitch Lake in Trinidad determined that the HC exceeded that of a reference soil having “normal HC” for a loam. Although water moved rapidly through it, the Pitch Lake soil (PLS) remained dry due to water repellence. Treatment consisted of either of two red mud bauxite wastes mixed at 25 and 50% w/w with PLS at air dry. One of the bauxite wastes had undergone treatment with gypsum several years before and hence contained a greater proportion of calcium ions compared to the other red mud which contained more sodium ions. At 25% w/w the non-gypsum-treated red mud waste decreased HC of the PLS by 50%, and at 50% w/w caused a 10-fold decrease of HC on the PLS. The gypsum-treated red mud waste had no effect on the HC of the PLS. The drastic decrease in HC of the hydrocarbon-contaminated soil implies blocking of hydraulic channels by inorganic particles. The high levels of Na+ released in the Bayer beneficiation process dispersed and released fine < 5 mµ clay particles from the non-gypsum-treated red muds. This suggests that the rapid movement of aqueous pollutants in such hydrocarbon-polluted soils could be similarly curtailed under field conditions.
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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".