Bibliographic record
Abstract
Professor Saliah-Hassane earned a PhD in Electrical and Computer Engineering from McGill University in Montreal, and a Bachelor and Master of Applied Science degree from École Polytechnique de Montréal, Canada. He is currently teaching Informatics and Computer Networks and Security at TELUQ University in Montreal where he is researching Intelligent Distributed Systems and Mobile Robotics. Professor Saliah-Hassane has received many awards in recognition of his accomplishments, including many IEEE Education Society Certificates of Appreciation, and IEEE Education Society's EdWin C. Jones, Jr. Meritorious Service Award (2019). And aligned with his work on Distributed Embedded Systems, the IEEE Standards Association award with appreciation for chairing and contributing to the development of IEEE Standard 1876 – 2019 on “Networked Smart Learning Objects for Online Laboratories” (2019), the IEEE SA 2019 Emerging Technology Awarded to IEEE SA 1876 – 2019 Working Group. Under the Candidate's leadership as the Chair of the Montreal IEEE Education Society Chapter (2005 -2022), the Chapter received the “2019 Chapter Achievement Award for sustained contributions of innovative educational and professional activities in the community”.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.205 | 0.119 |
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".