Styling Imagery of Self-Propelled Harvester Based on Kansei Engineering
Bibliographic record
Abstract
To enhance the design methodology of agricultural machinery and better align it with users' needs, Kansei engineering is utilized to analyze the internal correlation of the perceptual imagery of self-propelled harvesters and the impact of styling elements on this imagery.The SD (semantic difference) scale is employed to investigate the perceptual imagery of typical samples.Principal component analysis (PCA) is conducted using SPSS software to identify the primary factors influencing the appearance styling of self-propelled harvesters: style factor and utility factor.The styling characteristics of the harvester are derived from three perspectives: product structure, part proportion, and level of detail richness.The relationship between styling elements and perceptual imagery is established through the application of quantification theory type I.By examining the styling image of the self-propelled harvester, designers can determine the optimal styling direction based on user demand, thereby enhancing the design process in terms of rigor and efficiency.
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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 | 0.000 | 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) | 0.015 | 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 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".