Bibliographic record
Abstract
A fuzzy rulebase is a model of a system, expressed as a collection of fuzzy if/then rules, whose predicates are words in nature language, given mathematical meaning by associating each word with a fuzzy set fuzzy rulebases have been proven to be powerful and intuitive tools for modeling a wide range of phenomena. The problem of how to construct fuzzy rulebases has been extensively explored. However, the problem of how to defined operations on fuzzy rulebases is still an open question. This paper addressed the second question. The purpose of this research project was to develop an algorithm for detecting and quantifying structure similarity between two fuzzy rulebases, which in turn give an approximate measure of the similarity between two systems. The proposed algorithm is based on linguistic gradient, which is a linguistic analogue of the gradient operator from calculus. For each of the two fuzzy rulebases, the gradient vector can be computed at each point in the linguistic space. Each n-dimension gradient vector is converted to a 2ndimension 3-level vector by thresholding its magnitude on each axis. Vectors lie on each axis are added to compute projection. The projection vectors of the two fuzzy rulebases are regranulated to the same granularity using weighted sum. The regranulated vectors are then compared using Euclidean distance formula. The Euclidean distance is normalized so that results of different pair of rulebases are directly comparable. Programs have been developed for C++ and Matlab to implement this algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 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".