Soil bin tests and discrete element modeling of a disc opener
Bibliographic record
Abstract
Soil disturbance and cutting force are two of the most common performance indicators for openers. These were investigated for a disc opener through measurements in an indoor soil bin and modeling using the discrete element method (DEM). In the soil bin experiments, the disc was tested at a constant depth of 37.5 mm and different tilt angles (0°, 10°, and 20°). Draft and vertical forces, and soil throw caused by the disc were measured. The DEM model was validated using the results from the experiments. The validated model was used to predict soil-cutting forces under various operational parameters. Both the experiments and the model showed an increasing trend of soil throw with the tilt angle. The model produced a decreasing trend for the draft force and vertical force, while the experiments did not show any particular trend. In comparison with the experimental results, the model results had relative errors of 10.5%, 1.9%, and 59.7% in predicting soil throw, draft and vertical forces, respectively. The draft force predicted with the model increased from 9.4 to 74.7 N following a polynomial equation when the gang angle of the disc was varied from 0° to 30°, and from 3.1 to 82.9 N following a polynomial equation as well when the working depth was varied from 12.5 to 75.0 mm. The model was able to produce well-defined trends of draft, vertical, and lateral forces of the disc opener.
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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 | 0.000 | 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".