Characterization of Transparent Soil for Unsaturated Applications
Bibliographic record
Abstract
Abstract Experimental characterization of unsaturated soils is of primary importance to further understanding of fundamental behavior, as well as allow for accurate modeling and predictions, of constitutive and field behavior. In the laboratory, the most common research methodology used to investigate the hydraulic behavior of unsaturated soils involves placing the unsaturated soil in a column apparatus with measurements of pore pressure and moisture content being made at discrete locations distributed along the elevation of column. These types of tests have provided many valuable insights into unsaturated flow phenomena; however, there are some limitations with this methodology including the discrete nature of the measurement points. In this paper, an alternative method is proposed which aims to combine the use of digital image analysis with a transparent soil to avoid the ambiguity of traditional boundary image measurements of moisture content in column experiments. At 100% saturation, the transparent soil particles appear invisible and allows for the ability to see through the soil mass. Any air bubbles will be visible within the soil voids and as a result, at varying degrees of saturation less than 100%, the soil will become progressively non-transparent. The relationship between pixel intensity of the unsaturated soil and degree of saturation is defined and validated. This relationship allows definition of the degree of saturation throughout the column profile thus giving the opportunity to verify and further develop constitutive models for unsaturated hydraulic behavior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".