Unified modeling of a tractor performance prototype based on ontology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A tractor is a type of agricultural machinery with complex structure and harsh operating conditions. It is evolving toward a large-scale, multifunctional, and intelligent system. Digital prototype technology is an effective approach for experts in multidisciplinary fields to collaborate in the development of new tractor products. Tractor performance prototype design is an important part of realizing digital tractor design. In the modeling process, the performance prototype models designed by experts have a problem with inconsistent expressions, making tractor digital design difficult to implement. This study aims to investigate the unified modeling of a tractor performance prototype. The design process of the tractor performance prototype was analyzed according to the characteristics of new tractor product development. Combined with the ontology modeling method, the construction process of the tractor performance prototype ontology was designed. Based on ontology metamodel theory, a multidisciplinary unified modeling method for a tractor performance prototype is proposed, and an ontology metamodel architecture was constructed. Using a wheeled tractor as an example, a performance prototype ontology was designed. Subsequently, an ontology model was created and verified in Protégé. The results indicate that the model can be used for the digital design of new tractor product development. An ontology model database was established, which realized the sharing and management of ontology model data, and the effectiveness of the method was verified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it