Editorial Introduction of New Editor-in-Chief (EIC) and Deputy EIC
Bibliographic record
Abstract
After a brief review of the publishing statistics for the IEEE Embedded Systems Letters , the current Editor-in-Chief (EIC) introduces the new leadership team of Prof. Krithi Ramamritham as EIC and Catherine Gebotys as Deputy EIC. This leadership emerged after an extensive search for the EIC earlier this year. Prof. Ramamritham is an Endowed Chair Professor of Computer Science and Engineering at the Indian Institute of Technology, Bombay, and the author of almost 500 papers spanning many areas of embedded systems including real-time systems, distributed systems, databases, and sensor networks. Prof. Ramamritham is a researcher par excellence to lead the journal, and brings a wealth of experience in journal leadership from his tenure as EIC of the Real-Time Systems Journal and has been associated with many editorial boards. Dr. Catherine Gebotys is a Professor and a Professional Engineer with the Department of Electrical and Computer Engineering at the University of Waterloo, Waterloo, ON, Canada, and is the author of over 100 papers in the areas of electronic design automation, embedded systems security, and low-power design, Dr. Gebotys brings a wealth of research and practical experience built over two decades in the industry and academia.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.053 |
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".