Editorial: Fuzzy Logic and Artificial Intelligence: A Special Issue on Emerging Techniques and Their Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The eighteen papers in this special section focus on emerging techniques and applications supported by fuzzy logic and artificial intelligence (AI). AI has become the focus of the day and attracted much attention from researchers, industries, and governments. This special issue serves as a forum to bring together all emerging techniques for fuzzy logic and fuzzy set-based AI and foster new advancements along this important direction. Actually, there have been a number of research pursuits that position themselves at the junction of AI and fuzzy logic. For example, natural language processing, viewed as the jewel in the crown of AI, has been one of the focal points in the domain of fuzzy logic and fuzzy sets. Fuzzy sets can offer an effective paradigm supporting accurate understanding of natural language and build efficient linkages to human intelligence through concepts and computing with membership functions, in particular type-2 fuzzy sets for explainable AI.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it