Impact of Load and Inflation Pressure on Traffic-Induced Soil Compaction for Two Types of Flotation Tires
Bibliographic record
Abstract
Abstract. An Automatic Air Inflation-Deflation (AAID) control unit was evaluated on a manure tanker equipped with Alliance model 390 30.5LR32 steel belted radial-ply and Michelin 650/75R32 172A8/172B TL MEGAXBIB radial-ply flotation tires. The objective was to validate the effectiveness of the AAID control unit from an engineering perspective and its benefits for field agricultural operations. The contact patch was characterized in terms of rut depth and width, and tire contact length. Soil cone index was used as an indicator of soil compaction caused by the weight of the manure tanker. Cone index and rut depth at the centerline and edge of both the Alliance and the Michelin tires were affected by tire inflation pressure and load. As inflation pressure or load increased, the cone penetration resistance in the contact patch increased, indicating an increase in soil compaction. The adjusted tire inflation pressure for field operation using the AAID control unit reduced the rut depth at a lug imprint at the tire centerline, at a high load of 68 kN per tire, by 18.4% and 15.6% for the Michelin and Alliance tires, respectively, and by 19.1% and 12.0% at a low load of 44 kN per tire for the Michelin and Alliance tires respectively. Keywords: Automatic air inflation deflation, Cone index, Contact patch, Flotation tire, Manure tanker, Rut depth, Rut width, Soil compaction management.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".