Bibliographic record
Abstract
S loosening is a very important performance indicator forsub-soiling tools. In this study, soil loosening effects from a ripper (a sub-soiling tool) was investigated through numerical modeling. To assist the model development, tests of the ripper were performed in a field with a clay soil texture. In the tests, the ripper was operated at a tillage depth of 300 mm and travel speed of 3 km/h. Before testing, soil cone indices of the undisturbed field were measured using a cone penetrometer; after testing, soil cone indices of the disturbed soil resulting from the ripper passage were measured. A soil-ripper model was developed to simulate the field operation of the ripperand its interaction with soil using the discrete element method (DEM). The model was able to predict soil swell factor which is commonly used to evaluate the extent of soil loosening by a tillage tool. The model ripper was 1:1 scale representation of ripper used in the field tests, and the spherical model soil particles had diameters varying from 3 to 30 mm. The soil-ripper model was calibrated and validated through comparing soil cone indices measured in the field and thoseobtained through virtual penetration tests performed to the assembly of the model particles. The validated soilripper model was used to further investigate soil swell factor as affected by the ripper working depths under differentinitial soil porosities.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".