FIMIS-2013 Organizing Committee
Bibliographic record
Abstract
Program Committee Members Feilong Tang, Shanghai Jiao Tong University, China Fatos Xhafa, Technical University of Catalonia, Spain Arjan Durresi, IUPUI, USA Hae-Duck Joshua Jeong, Korean Bible University, Korea Hiroaki Kikuchi, Tokai University, Japan Chu-Hsing Lin, Tunghai University, Taiwan Jinshu Su, National University of Defense Technology, China Tomoya Enokido, Rissho University Fang-Yie Leu, Tunghai University, Taiwan Chunqing Wu, National University of Defense Technology, China Makoto Ikeda, Fukuoka Institute of Technology, Japan Takuo Suganuma, Tohoku University, Japan Antonio Gentile, University of Palermo, Italy Wen-Tzeng Huang, Minghsin University of Science and Technology, Taiwan Marek Ogiela, Computer Science and Electronics Insitute of Automatics, Poland Kin Fun Li, Victoria University, Canada Joan Arnedo, Open University of Catalonia, Spain Jong-Hyouk Lee, INRIA, France Hanh Le, University of Cape Town, South Africa Vidyasagar Potdar, Curtin Business School, Australia Eleana Asimakopoulou, University of Bedfordshire, United Kingdom Hiroshi Matsuno, Yamaguchi University, Japan Yoshitaka Shibata, Iwate Prefectural University, Japan Misako Urakami, Oshima College of Maritime Technology, Japan Tetsuya Shigeyasu, Hiroshima International University, Japan Nik Bessis, University of Bedfrordshire, United Kingdom Kangbin Yim, Soonchunhyang University, Republic of Korea Yoshiaki Hori, Kyushu University, Japan
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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; both teacher heads agree on what is shown here.
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".