Bibliographic record
Abstract
Nicola Nicolici: Good evening and I would like to welcome Janusz Rajski, a Life Fellow of IEEE, who received the Ph.D. degree in electrical engineering from Poznan textasciiacute University of Technology, Poland, in 1982. He is currently the vice president of engineering at Siemens Tessent Wilsonville, Wilsonville, OR, USA. During his tenure at Siemens, he has built a strong international research and development organization with a focus on innovative DFT technologies. His team has developed several revolutionary products widely adopted by the semiconductor industry: TestKompress, cell-aware test, and streaming scan networks. He has published 300 IEEE research papers and is a co-inventor of 130 U.S. and international patents. His papers won prestigious awards, including two best paper awards published in IEEE Transactions on CAD, one on logic synthesis and another on test compression. In 2009, Janusz received the Stephen Swerling Innovation Award from Mentor Graphics for his breakthrough innovation TestKompress and revitalizing Mentor’s DFT business to its current position as the number one test business in EDA. In 2018, he received the Siemens Inventor of the Year Lifetime Achievement Award for his extensive contributions to DFT. In 2023, he received the prestigious Bob Madge Innovation Award. Welcome, Janusz.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".