Soil Adhesion Preventing Mechanism of Bionic Bulldozing Plates and Mouldboard Ploughs
Bibliographic record
Abstract
Abstract: Soil adheres to the surfaces of soil engaging components of various tools. The adhesion of soil increases the draft and adversely affects the quality of work. For example, up to 50 % of the gross energy required for tillage operations may be consumed by adhesion and friction between soil and tillage tools. Therefore, it is important to find out the ways to reduce adhesion of soil to the surfaces of various tools. Soil animals such as ground beetles stay in moist sticky soils for extended periods without soil sticking to their bodies. The soil adhesion preventing mechanisms of such animals can be used as guide for improving the scouring properties of various tools. Both the surface morphology and chemical composition of soil animal’s cuticle play important role in preventing adhesion of soil to their bodies. The surfaces of mouldboard ploughs and bulldozing plates were modified based on the surface morphology of ground beetle and tested in the laboratory. Two materials such as Steel-45 and Ultra High Molecular Weight – Polyethylene (UHMW-PE) were used for convexes. The modified ploughs and plates have better scouring properties and required less draft than conventional tools. The size of convexes, their arrangement and the material of these convexes played
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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 | 0.000 |
| 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) | 0.002 | 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".