Optimization of the Tractive Performance of Four-Wheel-Drive Tractors - Correlation between Analytical Predictions and Experimental Data
Bibliographic record
Abstract
Analytical studies reveal that for a four-wheel-drive tractor with rigidly coupled drive axles to achieve the optimum tractive performance under a given operating condition, the theoretical speed (the product of angular speed and free rolling radius) of the front tires must be equal to that of the rear tires, or the theoretical speed ratio must be one. This paper presents tractive performance test data obtained using an instrumented four-wheel-drive tractor with seven different sets of tires at various theoretical speed ratios. Field data confirm the analytical findings that when the theoretical speed ratio is equal to one, the slip efficiency and tractive efficiency reach their respective peaks, the fuel efficiency (the ratio of drawbar power to fuel consumed per hour) reaches a maximum, and the overall tractive performance is at an optimum. It is concluded that to achieve optimum tractive performance in the field, proper matching of front and rear tire sizes and careful control of the inflation pressure and normal load of the tires to ensure the theoretical speed ratio equal or close to one are of practical importance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".