Bibliographic record
Abstract
I am delighted to introduce this Mini-Special Issue of the IEEE Transactions on Microwave Theory and Techniques (TMTT), which showcases selected papers from the International Conference on Numerical Electromagnetics, Multiphysics Modeling and Optimization (NEMO 2023). The NEMO 2023 took place on June 28–30, 2023 at the historic Fort Garry Hotel in the heart of Winnipeg, MB, Canada. NEMO is an annual international event founded in 2014 and has become a popular venue for exchanging novel ideas in computational and applied electromagnetics, multiphysics simulations, and optimization methods. Alternating between North America, Asia, and Europe, the conference brings together leading experts from academia and industry within the IEEE Microwave Theory and Techniques Society (IEEE MTT-S) and beyond. In particular, broad participation was achieved this year due to the availability of both online and in-person formats for attendance. The participants of the NEMO 2023 had the opportunity to attend the technical sessions and social events and to establish closer personal contacts. The NEMO 2023 was sponsored by the Price Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada, and industry partners Xpeedic, Ansys, CEMWorks, and Sonnet Software.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.078 | 0.068 |
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".