Effects of cutting parameters on the ultimate shear stress and specific cutting energy of Canadian goldenrod stem
Bibliographic record
Abstract
Summary Due to the lack of weed-specific designs in weeding equipment, this article conducted cutting experiments on one type of weed: Canadian goldenrod. This study examined the influence of various cutting parameters on the process of cutting Canadian goldenrod stems to determine the optimal cutting parameters. A quasi-static cutting method was used to cut the stems. The results indicated that cutting speed, stem oblique angle, blade oblique angle, and stem diameter significantly affected the ultimate shear stress and specific cutting energy during the cutting process. The response surface method was employed to explore the optimal cutting parameters. In this experiment, the minimum combined values of ultimate shear stress and specific cutting energy were achieved when the cutting speed, stem oblique angle, blade oblique angle, and stem diameter were 30 mm s -1 , 20°, 0°, and 6 mm, respectively. When cutting close to the ground, the optimized cutting method reduced the ultimate shear stress and specific cutting energy by 52.68% and 55.22%, respectively, compared to ordinary cutting. These optimal cutting parameters can support the blade layout and output power design of a weeder or harvester.
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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) | 0.001 | 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".