Guest Editorial Special Issue on EPEC 2020 Éditorial Invité Numéro Spécial sur EPEC 2020
Bibliographic record
Abstract
Welcome to a Special Issue of the IEEE Canadian Journal of Electrical and Computer Engineering (ICJECE), which presents some of the top articles (in extended form) from the Electric Power and Energy Conference (EPEC 2020) held on November 9–12, 2020. The theme was “Power Systems in Transition.” This conference was originally planned to take place at the University of Alberta, Edmonton, AB, Canada, but the COVID-19 global pandemic required pivoting to a virtual (online) platform. This marks the first time this conference was held remotely. EPEC 2020 was hosted by the Northern Canada Section, with five cosponsors (IEEE Canada, IEEE Southern Alberta Section, IEEE North Saskatchewan Section, IEEE Winnipeg Section, and the IEEE IAS/PES Northern Canada Chapter) and two technical cosponsors (IEEE PES and IEEE PELS).
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.086 | 0.061 |
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".