Bibliographic record
Abstract
I would like to express my sincere gratitude to my thesis advisor, Dr. Eamonn J. Keogh for his invaluable guidance and support during my graduate studies at the University of California, Riverside. He always encourages my progress, new ideas, as well as motivates my research work. He is always full of energy and untiringly available for giving me insightful advices. In addition, I have learned precious lessons through his past and present remarkable research work, as well as his exceptional presentations. I truly consider it a great privilege in having the opportunity to work with him as my graduate advisor. I am indebted to Dr. Dimitrios Gunopulos who was the person introducing me to the Data Mining research area in the first place. I took a data mining seminar class with him in the first quarter I attended UCR, giving me a chance to start thinking more into this particular research area. Later in the following year, I had a wonderful opportunity to be a teaching assistant for Dr. Stefano Lonardi for his Data Structure class. Not only did he become a co-author of my several great papers, but his advice and suggestions have also helped me developed proficiently. I am also grateful to Dr. Michalis Faloutsos
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.843 | 0.871 |
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; the direct Gemma label and the distilled Codex classifier 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".