Bibliographic record
Abstract
Jess, the Java Expert System Shell, provides a rich and flexible environment for creating rule-based systems. Since it is written in Java it provides platform portability, extensibility and easy integration with other Java code or applications. The rules of Jess allow one to build systems that reason about knowledge that is expressed as facts. However, these facts and rules cannot capture any uncertainty or imprecision that may be present in the domain that is being modelled. This paper describes an extension to Jess that allows some forms of uncertainty to be captured and represented using fuzzy sets and fuzzy reasoning. We describe the NRC FuzzyJ Toolkit, a Java API that allows one to express fuzzy concepts using fuzzy variables, fuzzy values and fuzzy rules. Next, we describe a Java API called FuzzyJess that integrates the FuzzyJ Toolkit and Jess. Finally, we show the modifications that were made to the Jess code to allow this extension (and others with similar requirements) to be added with modest effort and with minimal or no impact as new releases of Jess are delivered.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".