Formal description of the ADT-model of B-trees
Bibliographic record
Abstract
Formal specification of abstract data types (ADTs) is important in modeling system architecture and their implementations. B-Trees are one of the most widely used ADT in system development. This paper presents a formal approach to the specification of B-Tree using real-time process algebra (RTPA), which is a newly developed mathematics-based notation system for the specification and refinement of real-time and safety-critical systems. The logical model of B-Tree has been abstracted first. The RTPA specification of B-Tree is based on the logical model. In the RTPA specification, B-Tree has been formally described in three parts: system architecture, static behaviors, and dynamic behaviors. In the architectural specification, both logical model and physical implementation model of B-Tree has been specified. The logical model of B-Tree uses RTPA component logical models to present the structure of a B-Tree by nodes and characteristics; while the physical implementation model of B-Tree uses linked list to describe the implementation of a B-Tree. In the behavioral specification, three kinds of B-Tree behaviors, namely traversal operations, manipulation operations, and query operations, have been abstracted and specified by RTPA processes. This work is a part of the effort to build an ADT library for RTPA in the RTPA-based code generation project.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".