Development of semi-physically based model to predict erosion rate of kaolinite clay under different moisture content
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it