Performance of wheel and track running gear on liquid manure spreaders
Bibliographic record
Abstract
McBride, R.A., McLaughlin, N.B. and D.W. Veenhof. 2000. Performance of wheel and track running gear on liquid manure spreaders. Can. Agric. Eng. 42:019-025. A field experiment was conducted to characterize soil-vehicle interactions and to measure tractor power/fuel requirements when hauling a fully loaded tank spreader (18 m) fitted with rubber tracks (2721x635), high flotation tires (28L26) or conventional truck tires (445/65R22.5). A second objective was to apply an existing soil compaction model to the data from this field trial and to determine if its estimates of wheel rut depth were reasonable. The experiment was carried out in late autumn on a harvested soybean field (silt loam soil) located in southwestern Ontario. Fuel consumption and drawbar draft were measured during the traffic treatments with an instrumented tractor, and selected soil properties were measured afterwards. An analytical-type soil compaction model was used to estimate the wheel rut depth for the two pneumatic tire treatments. The type of running gear had a highly significant effect on both drawbar draft and fuel consumption (p < 0.001), with the tracks having the highest values (mean 24.6 kN and 21.5 L/h, respectively) and the flotation tires having the lowest (mean 13.9 kN and 16.9 L/h, respectively). The truck tires left ruts with a mean depth of 42.3 mm, while the flotation tires left cleat impressions that were barely discernable. A pedotransfer function was used to estimate the preconsolidation stress (26 kPa) and compression index (0.173) of the plow layer soil and, with these and other input data, the soil compaction model estimated rut depths that were quite comparable to those observed. The only ruts produced by the tracks were those of the track grousers (about 48 mm deep), but extensive soil shearing was evident. The tracks and truck tires produced significantly higher (p < 0.05) measured dry bulk densities at a depth of 150 mm when compared to those induced by the flotation tires. Soil cone penetrometer measurements were not as conclusive in distinguishing the impact of the running gear treatments on soil structural conditions. In general, however, flotation tires appeared to be the preferred running gear option with respect to several key parameters (fuel consumption, drawbar draft, wheel rut depth, dry bulk density) under these particular soil and loading conditions.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".