Object-Centered Engineering: A Methodology for Virtual Engineering
Bibliographic record
Abstract
In the product realization process, it is not currently possible to go from business case models to the final production system in the virtual design space. Virtual engineering aims to address this problem. Virtual engineering techniques will allow users to track the production of a product or system from birth to death, from the complete business case model to the customer’s feedback on the first production run. To implement a framework that will handle the broad range of information that is necessary to track a product through its complete life cycle, an object-centered approach involving virtual objects is required. These virtual objects will represent the physical objects as they exist in the “real world.” To enable these objects to have extensible qualities similar to objectoriented principles, similar techniques to those used by object-oriented design will be employed. Some of these techniques include multi-representational models, hierarchy, inheritance, and dynamic access. One important justification for an object-centered approach is that it gives stakeholders and engineers a mechanism for discourse regarding the product or system under design. Giving the engineer and other collaborators a comfortable and familiar mechanism by which to share and discuss ideas is crucial in allowing users to gain understanding about a product’s key issues. These ideas and processes are embodied in virtual engineering and the method behind it, which is referred to as objectcentered engineering. These ideas and the object-centered method will be discussed in this paper.
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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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".