ECPIA: Environnement de calcul pan-canadien pour l'intelligence artificielle - 2025 DRI Connect poster
Bibliographic record
Abstract
Poster created for DRI Connect 2025 (French version). --- C’est en réponse aux besoins actuels et émergents de la communauté scientifique canadienne en matière d’IA que l’initiative de l’Environnement de calcul pancanadien pour l’IA (ECPIA) a vu le jour dans le cadre de la phase 2 de la Stratégie pancanadienne en matière d’intelligence artificielle, et ce en vue de développer une infrastructure nationale d’IA spécialisée, et doter celle-ci des ressources de calcul et des services connexes précis. L’initiative ECPIA est une collaboration sous la houlette d’une coalition composée de l’Alliance, du Canadian Institute for Advanced Research (CIFAR), des Instituts d’IA nationaux du Canada (Amii, Mila et l’Institut Vecteur), des organismes régionaux de calcul informatique de pointe (CIP), ainsi que de Calcul Québec, et bénéficiant de l’infrastructure clé et de l’expertise technique de l’Université Laval, de l’Université de l’Alberta et de l’Université de Toronto. Cette approche collaborative a été conçue pour s’assurer que tous les organismes, y compris la communauté de recherche canadienne, jouent un rôle clé dans les efforts d’approvisionnement, de gouvernance et de surveillance continus liés à l’infrastructure d’IA nationale.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".