Bibliographic record
Abstract
R ecently, the subject of patterns has become one of the hottest topics in object-oriented development. The most well-known patterns are the design patterns of the Gang of Four (Gamma, E., R. Helm, R. Johnson, and J. Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software , Addison-Wesley, 1994), fundamental implementation patterns that are widely useful in object-oriented implementations. An important part of most OO systems is the domain model—those classes which model the underlying business that the software is supporting. We can see patterns in domain models too, patterns that often recur in surprising places. I will provide a short introduction to some analysis patterns that I have come across. I'm not going to do this by surveying the field, or discussing theory—patterns don't really lend themselves to that. Rather, I'll show you some examples of patterns and illustrate them with UML diagrams (Fowler, M., UML Distilled: Applying the Standard Object Modeling Language , Addison-Wesley, 1997) and Java interface declarations. REPRESENTING QUANTITIES Imagine you are writing a computerized medical record system for a hospital. You have to record several measurements about your patients, such as their height, weight, and blood-glucose level. You might choose to model this using numeric attributes (see Figure 1 and Listing 1). This approach is quite common, but may present several problems. One major problem is with using numbers.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".