Bibliographic record
Abstract
In this paper we present Jedd, a language extension to Java that supports a convenient way of programming with Binary Decision Diagrams (BDDs). The Jedd language abstracts BDDs as database-style relations and operations on relations, and provides static type rules to ensure that relational operations are used correctly.The paper provides a description of the Jedd language and reports on the design and implementation of the Jedd translator and associated runtime system. Of particular interest is the approach to assigning attributes from the high-level relations to physical domains in the underlying BDDs, which is done by expressing the constraints as a SAT problem and using a modern SAT solver to compute the solution. Further, a runtime system is defined that handles memory management issues and supports a browsable profiling tool for tuning the key BDD operations.The motivation for designing Jedd was to support the development of whole program analyses based on BDDs, and we have used Jedd to express five key interrelated whole program analyses in our Soot compiler framework. We provide some examples of this application and discuss our experiences using Jedd.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.018 |
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".