Discrete element modelling of soil pressure under varying number of tire passes
Bibliographic record
Abstract
Pressure imposed on an arable farmland by farm machinery can lead to severe soil compaction. A model was developed with Discrete Element Method (DEM) to simulate soil – tire interaction. Virtual dead weight method was performed for the purpose of model calibration. Simulated soil pressure data were obtained from the topsoil layer under varying number of tractor tire passes (1P, 2P, 3P, 4P, 5P, 6P, 7P and 8P). Simulation results were validated with maximum soil pressure data from a field experiment in which soil pressure was measured at 0.1 m depth in sandy loam soil. Model results of maximum soil pressure increased from 137.7 to 242.5 kPa when the number of passes increased from 1P to 8P. Prediction of the maximum soil pressure was reasonably accurate for 1P and 2P with Relative Mean Errors (R.M.E) less than 9%. Predictions for 3P to 8P had higher R.M.E. In terms of model application, soil sinkage and rolling resistance ranged from 0.07 to 0.14 m and 225.3 to 517.8 N respectively between one to eight passes. The model developed in this study can be used in the simulation of soil pressure distribution and deformation in the topsoil layer induced by heavy farm machinery.
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 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".