Development of semi-physically based model to predict erosion rate of kaolinite clay under different moisture content
Bibliographic record
Abstract
Understanding the susceptibility of soils to concentrated flow erosion is imperative for predicting sustainability of various engineering structures and assessing environmental integrity. Currently, a widely used model is empirical in nature. In this study, we developed a semi-physically based model that predicts the rate of concentrated flow erosion of kaolinite clay based on tensile and erodibility characteristics. To develop this model, direct tensile tests and jet erosion tests (JETs) were performed on kaolinite clay with different percent moisture contents (MCs). The direct tensile test results showed that the energy required to break interparticle bonds across a fracture plane and tensile strength decreases with an increase in MC, whereas the JET results showed that soil resistance to erosion decreases with an increase in MC. Results also showed that an efficiency index of the JET apparatus, which represents the fraction of jet power used in actual erosion processes, diminishes with a decrease in MC. This semi-physically based model predicted the rate of erosion of kaolinite clay for a range of MC and applied hydraulic shear stress. In model development and verification, 98% and 90% of the data, respectively, were within a discrepancy ratio of 0.50 and 2.0.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".