34th Electrical Insulation Conference report
Bibliographic record
Abstract
The 34th Electrical Insulation Conference was held June 19 to 22, 2016, at the Hotel Bonaventure in Montreal, Canada. Even in tough economic times, Montreal is still popular; we were able to hold the line on attendance, with 259 participants from different countries covering all major continents. A total of 311 abstracts were submitted, resulting in a final program of 151 papers, 49 of which were presented in two poster sessions. The conference opened on Sunday with two fullday and one half-day short courses offered by technical experts. These courses were On-Site and Laboratory Partial Discharge by Tom Prevost and William Hu, Effect of Inverter Fed Drives on Rotating Machine Electrical Insulation by Greg Stone, and Forensic Analysis of Insulation and System Failures by Howard Penrose. The first poster session, with 30 posters on display, was held on Sunday evening as part of the welcoming reception. The oral sessions opened on Monday morning. Bernard Noirhomme, the Conference Chair, welcomed the attendees and then yielded the floor to Stephane Lemieux, Scientific Director of the Research Institute of Hydro-Quebec, who talked about the importance of research on electrical insulation for Hydro-Quebec.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.290 | 0.162 |
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".