Bibliographic record
Abstract
This year, the annual Townhall Meeting of the IEEE Power Electronics Society (PELS) was an in-person event, held on 11 October 2022, as part of the IEEE Energy Conversion Congress & Expo (ECCE), at the Huntington Place, Detroit, MI, USA. It began with a 35th anniversary reception, followed by meeting and discussion in a hall. PELS President Liuchen Chang, professor emeritus, University of New Brunswick, Canada, kicked of the Townhall Meeting with a slide presentation that highlighted 35 years of growth and excellence. Since the establishment of PELS 35 years ago, thousands of volunteers have been working on the growth and excellence of our society, stated Chang. As a result, he added, today there are 11,000+ members world-wide, and 233 chapters, joint chapters, and student branch chapters in all 10 regions of IEEE (Figure 1). With growing applications of power electronics, the society has created 12 technical committees (TCs) to give a home to all power electronics professionals, noted Chang. The slides indicated that today there are 3500+ members participating in the 12 TCs, and the number continues to grow.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.213 | 0.123 |
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".