Electric Conductivity Probes to Study Change in Degree of Saturation - Bench Top Laboratory Tests
Bibliographic record
Abstract
Sand characteristics such as liquefaction susceptibility can be affected as a result of change in degree of saturation of sand. New liquefaction mitigation technique by inducing partial saturation in sands is introduced by Yegian et al in 2007[1]. This technique requires to monitor changes in degree of saturation of sand. By nature, changes in degree of saturation of sand can lead in changes in its electric conductivity. Electric conductivity is the property of a material that represents its ability to conduct electric current. Fully saturated sand can conduct electric current better than sand with lower degree of saturation. Therefore, the change in measured electric conductivity can be used to calculate the change in degree of saturation of sand. In 1942, Gus Archie [2] expressed that the electric conductivity of soil is a function of its porosity, degree of saturation, tortuosity and electric conductivity of pore fluid. Using Archie’s law electrical conductivity can be related to the degree of saturation in sands. Typically, electric conductivity probes and meters are instruments which are used to measure electric conductivity. Using electrical conductivity probes, sets of bench top tests were conducted on Ottawa sand to study the relation between degree of saturation and electric conductivity in sand. Partial saturation in sands were created by pouring dry sand into sodium percarbonate solution with a known initial concentration. By nature, sodium percarbonate in water, generates oxygen gas bubbles in time. The changes in electric conductivity in the specimen were measured using electric conductivity meters and probes. In addition, changes in degree of saturation of the specimen were measured using soil phase relations equations. Measured electric conductivity data and calculated degree of saturations were correlated to explore relation between electric conductivity and degree of saturation. This paper presents results of bench top tests, and suggests a relationship between, final degree of saturation of sand and initial concentration of sodium percarbonate solution
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".