A Case Study for Evaluation of GUTD on Soil Compaction and Tractor Performance
Bibliographic record
Abstract
<p>Machinery traffic on farm and forest caused to problems in environment and economics by Fossil fuel consumption and soil compaction. According to result of investigation, The GUTD (Gear Up and Throttle Down) method was recommended to decrease fuel consumption. But the wide range of machinery applications, soil conditions and experience of drivers or operators need to have index for ensuring the GUTD method is carried out properly. The purpose of this study was evaluation of GUTD method using MF 399 tractor on instantaneous fuel consumption and soil compaction. The tractor was equipped by sensors, a designed electronic circuit and a computer to measure and record parameters such as fuel consumption, forward speed and wheel slip. This field examination was carried out on tilled land with clay-loam soil and then data were analyzed in a randomized complete block design with three replications. Considered factors were included four speed levels (1.6, 3.2, 6.4, 12.8 km h<sup>-1</sup>), three levels of gear (first, second and third) and two levels of wheel drive (2WD and 4WD). Parameters of cone index and bulk density were used to determine soil compaction.Variations in compaction of before and after tractor passage were calculated in each plot. Statistical analysis in each speed showed that factor of gear in variations of soil compaction was significant at all speeds except 12.8 km h<sup>-1</sup> and instantaneous fuel consumption was reduced by GUTD at each of constant speed except the mentioned speed. This research showed that instantaneous fuel consumption can be considered as appropriate index for selecting the best gear and speed in terms of fuel consumption and soil compaction.<strong></strong></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".