Bibliographic record
Abstract
The amount of energy consumed during a tillage operation depends on three categories of parameters: (1) soil parameters (2) tool parameters and (3) operating parameters. Although many research works have been reported on the effects of those parameters on tillage energy, the exact number of affecting parameters and the contribution of each parameter in total energy requirement have not been specified. A study with the objectives of specifying energy consuming components and determining the amount of each component for a vertical narrow tool, particularly at high speeds of operation, was conducted in the soil bin facilities of the Department of Agricultural and Bioresource Engineering, University of Saskatchewan. Based on studies by Blumel (1986) and Kushwaha and Linke (1996), four main energy consuming components were assumed: (1) energy requirements associated with soil-tool interactions;(2) energy requirements associated with interactions between tilled and fixed soil masses;(3) energy requirements associated with soil deformation; and (4) energy requirements associated with the acceleration of the tilled soil. Energy requirement of a vertical narrow tool was calculated based on the draft requirement of the tool measured in the soil bin. The effects of three variables, moisture content, operating depth and forward speed, were studied at different levels: (1) moisture content at 14% and 20%; (2) depth at 40, 80, 120 and 160 mm; and (3) speed at 1, 8, 16 and 24 km h-1. Total energy requirement was divided into these four components based upon the procedure developed in the research. Regression equations for different energy components were developed based on experimental data of two replicates and then validated by extra soil bin experiments conducted at same soil and tool but different operational conditions. The set up of energy components data in the model development showed good correlation with the available experimental data for all four components. Coefficients of all regression equations showed a first order energy-moisture content relationship best applicable to those equations of energy components. For the acceleration component, energy-depth relationship at all speed levels resulted in an equation which included first and second orders of depth. In contrast, if only two higher levels of speed were used in the regression model, the relationship between acceleration energy and depth resulted in the second order of depth. When experimental data of acceleration energy at 8, 16, and 24 km h-1 speeds were used in the regression equation, the acceleration energy-speed relationship resulted in both linear and quadratic relationships. It was concluded that for the tool and soil conditions used in the experiments, 8 km h-1 speed resulted in only linear relationship. On the other hand, 16 and 24 km h-1 speeds resulted in a quadratic relationship. Therefore, for all 3 speeds used in experiments, both linear and quadratic relationships were obtained. Considering that the tool was operating at high speeds, this…
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".