The Effect of Soil Composition and Moisture Content on Dry Density and Hydraulic Conductivity of Clays
Bibliographic record
Abstract
Soil composition and moisture content have significant effects on the resulting ulk density of clayey soils because of the rearrangement of the solid particles and chemical stabilization. In this study, kaolinite and kaolinite with calcium carbonate (KC), silica gel (KS), and both calcium carbonate and silica gel (KSC) were compacted at different moisture contents according to ASTM standards. These mixtures of compacted clays were then subjected to leaching by distilled water, followed by a solution of heavy metals. Experimental results indicate that the dry density and coefficient of hydraulic conductivity are significantly influenced by the soil constituents. Specifically, kaolinite exhibits a low dry density and a high coefficient of permeability when compared to the other soils (KS, KC and KSC). As permeant flow approaches a stationary regime, greater pore volumes of effluents result, and an increase in the coefficient of permeability is observed in all types of soils. Tests confirm that chemical reactions are responsible for permeability increases up to a constant value. Expressly, kaolinite mixtures with silica gel or calcium carbonate exhibit a coefficient of permeability almost one order less than that of kaolinite. Results indicate the importance of silica gel as an additive for the reduction of the permeability in clay soils.
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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".