Bibliographic record
Abstract
This appendix summarizes the Unified Modeling Language (UML) diagrammatic notation employed throughout this book along with the associated terminology and brief definitions of each term. We consider the elements that appear in the five types of UML diagrams used in the body of the current text: use case, class, object, package, and sequence diagrams. At the end of the appendix, we give a brief discussion of Object Constraint Language (OCL), a declarative language for describing rules for UML models. Use Case Diagrams A use case is a description of a system's behavior as it responds to an outside request or input. It captures at a high level who does what for the system being modeled. Use cases describe behavior, focusing on the roles of each element in the system rather than on how each element does its works. A use case diagram models relationships between use cases and external requests, thus rendering a visual overview of system functionality. Figure B.1 reexamines the fin heat conductor analyzer diagram from Figure 2.6, adding notations to identify the elements of the use case diagram. Use case diagrams commonly contain the following elements: Actors: people or external systems that interact with the system being modeled. Actors live outside the system and are the users of the system. Typically actors interact with the system through use cases. In UML, actors are drawn as stick figures. In the fin analyzer system example, system architect, thermal analyst , and numerical analyst are actors. […]
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 0.054 |
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".