Bibliographic record
Abstract
Abstract Computer‐aided design (CAD) involves the use of computers in the various stages of engineering design. CAD has large volumes of data with complex structures that need to be stored and managed effectively and properly. Database systems provide general purpose programs that can be used to access and manipulate large amounts of data stored in the database. They also provide an independence between the program accessing data and the database. It is therefore important to use database systems to store CAD data in the most efficient and effective manner for easy retrieval and better management. Graphical objects can be created, in CAD, by reusing previously created objects. The data of these objects have references to the other objects they contain. Deductive object‐relational databases not only provide direct support for the effective storage and efficient access to large amounts of data with complex structures on disk, but also perform the inferences and computations to obtain the complete data of graphical objects that reuse other objects. They should be able to play a major role in CAD systems. This is the idea behind the development of the DrawCAD system. DrawCAD is a CAD system built on top of the Relationlog object‐relational deductive database system. It facilitates the creation of graphical objects by reusing previously created objects. The DrawCAD system illustrates how CAD systems can be developed, using database systems to store and manage data and also perform the inferences and computations that are normally performed by the application program. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".