Applying geometric compatibility modification in FuzzyCLIPS
Bibliographic record
Abstract
A new form of fuzzy reasoning, geometric compatibility modification (GCM) inference, is currently under investigation through its implementation within FuzzyCLIPS, a fuzzy systems development tool from the National Research Council of Canada. Using the dissemblance compatibility measure, GCM inference is able to determine a conclusion or an action even when the fuzzy input does not intersect the fuzzy antecedent of any rule. This situation can occur in sparse rule bases. The initial goal of GCM implementation within FuzzyCLIPS is to compare its performance to the standard fuzzy inference technique with compact rule bases. The next goal is to capitalize on GCM's ability to infer a consequent even in sparse rule bases where conventional fuzzy reasoning methods are unable to derive one. The results and knowledge gained from the FuzzyCLIPS GCM implementation, and testing with a simple control problem are presented. The future plans for continued investigation of GCM inference are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".