Effect of Moisture Content on Rubber, Steel and Tetrafluoroethylene Materials Sliding on Textured Soils
Bibliographic record
Abstract
There is the need to evaluate soil parameters of Nigerian soil that are necessary in the design of suitable and appropriate soil engaging implements. Laboratory investigations were carried out to evaluate angle of soil/material friction (or coefficient of soil/material friction) necessary in the design of soil-engaging implements. Facility used in the investigation was soil-material friction device or sliding shear apparatus. The soils investigated were loamy sand (IGLS), sandy loam (H3) and clay (H2) soils. The materials tested were rubber (RUB), steel (SST), galvanized steel (GAS) and Teflon (TEF). Results showed that coefficient of soil/material friction increased with moisture content to a maximum and thereafter decreased. The value ranged from 0.13 to 0.85 in the three soil textures and the trend can be described by polynomial equations for the purpose of prediction. Rubber had the highest coefficient of soil/interface friction while Teflon had the least.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".