The Formal Design Model of Doubly-Linked-Circular Lists (DLC-Lists)
Bibliographic record
Abstract
Abstract Data Types (ADTs) are a set of highly generic and rigorously modeled data structures in type theory. Lists as a finite sequence of elements are one of the most fundamental and widely used ADTs in system modeling, which provide a standard encapsulation and access interface for manipulating large-volume information and persistent data. This paper develops a comprehensive design pattern of formal lists using a doubly-linked-circular (DLC) list architecture. A rigorous denotational mathematics, Real-Time Process Algebra (RTPA), is adopted, which allows both architectural and behavioral models of lists to be rigorously designed and implemented in a top-down approach. The architectural models of DLC-Lists are created using RTPA architectural modeling methodologies known as the Unified Data Models (UDMs). The behavioral models of DLC-Lists are specified and refined by a set of Unified Process Models (UPMs) in three categories namely the management operations, traversal operations, and node I/O operations. This work has been applied in a number of real-time and nonreal-time system designs such as a real-time operating system (RTOS+), a file management system (FMS), and the ADT library for an RTPA-based automatic code generation tool.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".