Optimal Weight Distribution of a Quarter Scale Tractor
Bibliographic record
Abstract
Abstract. For 27 years teams competing in the ASABE International ¼ Scale Tractor Competition have sought to increase the pulling performance of the quarter scale tractors they design each year. The purpose of this research was to determine the optimal weight distribution for a quarter scale tractor to maximize pulling distance. This would give teams guidance for future design work and insight into the importance of weight distribution. This was done with the development and use of a quarter scale tractor pull simulation. The simulation showed good results when compared to a quarter scale tractor from the University of Saskatchewan‘s Quarter Scale Tractor Team. From this simulation the optimal center of gravity (COG) for a 0-degree chain angle pull was 0.24 m in front of and 0.075 m below the centerline of the rear axle. For a 23-degree chain angle pull the optimal location was 0.39 m in front of and 0.275 m below the centerline of the rear axle. Significant changes in maximum pull distance were observed with changes in the location of the COG. Pull distance was found to decrease by 10% by moving the COG from the ideal location another 20 cm in front of the rear axle. Pull distance decreased by 70% by moving the COG 20 cm closer to the rear axle. This highlighted the importance of ballasting the tractor differently for different chain angles and the performance costs of moving the COG.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".