CESCA Newsletter
Bibliographic record
Abstract
Dr. Haibo Zeng has joined the Bradley Department of Electrical and Computer Engineering as an assistant professor. Zeng will work with the department’s Center for Embedded Systems for Critical Applications. His research interests include design methodology, analysis and optimization for embedded systems, real-time systems, and cyber physical systems. Zeng has co-authored two books, “Embedded Systems Development–From Functional Models to Implementations,” and “Understanding and Using the Controller Area Network Communication Protocol: Theory and Practice,” and has co-authored 17 peer-reviewed articles. He has more than 30 conference publications and earned three best paper citations, two at the IEEE Symposium on Industrial Embedded Systems in 2009 and 2011, and one at the Euromicro Conference on Real-Time Systems in 2013. He served as editor for two special IEEE publications: “System Level Design of Automotive Electronics/Software” in 2012 and “Automotive Embedded Systems” in 2010. Zeng earned his Ph.D. at the University of California at Berkeley. He previously served as an assistant professor at McGill University in Montreal, Quebec, Canada, from 2011 - 2014 and was a researcher and then a senior researcher at General Motors from 2008 - 2011.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.590 | 0.427 |
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".