Bibliographic record
Abstract
The use of electrokinetic treatment to decrease the water content and increase the shear strength, preconsolidation pressure and axial load capacity of a laboratory-prepared soft clay soil was investigated. The tests were carried out in four identical electrokinetic cells. The cell has a volume capacity of 10 litres. A DC voltage of 10 V was applied in the tests investigating the water content, shear strength and preconsolidation pressure. A DC voltage of 5 V was used in the tests investigating the axial load capacity. The energy consumption for each test was 54 W h. The electrokinetic treatment decreased the water content across most of the cell with the lowest water content near the anode (32·8 ± 2% in comparison with 52·7 ± 2·7% in the control) and increased the undrained shear strength across the cell with the highest shear strength reported near the anode (62·5 ± 6·2 kPa in comparison with 6·3 ± 2·1 kPa in the control). Electrokinetic treatment increased the preconsolidation pressure across the cell with the maximum pseudo-preconsolidation pressure near the anode (91 kPa in comparison with pre-loaded surcharge pressure of 10 kPa). The axial load capacity of the foundation model after the treatment was 156 N when the foundation model was serving as the anode and 173 N when the model was not used as an electrode. The loss in the mass of the foundation model by corrosion was 4·7% for the former and 0·4% for the latter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".