Special Issue on the Sixth International Conference on Fuzzy Systems (AFSS' 2004)
Bibliographic record
Abstract
This special issue features five papers devoted to fuzzy systems and their applications. Papers were selected from those accepted and presented at the Sixth International Conference on Fuzzy Systems (AFSS' 2004) held in Hanoi, Vietnam on December 15-17, 2004. AFSS' 2004 and Tutorials held in Hue city on December 18-19, 2004, included a wide spectrum of research topics on "fuzzy set theory", "intelligent technology", "fuzzy logic and approximate reasoning", "neural networks", "genetic algorithms", "hybrid systems" and "soft computing". Over 40 papers were accepted and presented by researchers from countries including Brazil, Canada, Taiwan, India, Korea, Malaysia, Japan and Vietnam. Five papers receiving outstanding recommendations in reviews have been selected for this issue. The topics they address include fuzzy logic for robots, data mining, neural networks in medicine, Fuzzy Constraint Satisfaction Problems, and hybrid systems. As editors of this special issue, we are sincerely grateful to the authors. Special thanks also go to the referees for their excellent work, to Mr. Kazuki Ohmori for his aid in coordinating the issue's publication, and to the JACIII Editorial Board, especially Professor Kaoru Hirota for his invaluable support and encouragement. Finally, we thank Professors Masao Mukaidono and Witold Pedrycz for their contributions to AFSS' 2004. Without their support, AFSS' 2004 and this issue would not have been possible.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.161 | 0.075 |
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".