Unified modeling of a tractor performance prototype based on ontology
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".