Étude de l’impact de la technologie blockchain sur l’efficacité de la réponse à la demande électrique dans la province du Québec
Bibliographic record
Abstract
Je tiens à exprimer à travers ses mots, ma profonde gratitude et reconnaissance: À mes parents, pour leur amour inconditionnel et leur soutien lors de ma formation à l'Université du Québec à Trois-Rivières.À mon directeur de recherche, le Prof. Skorek, Adam Waldemar, PhD, M. Sc.Eng., Fellow ICI., IEEE Life Fellow, et aux évaluateurs de ce document pour leurs apports significatifs à l'amélioration de ce travail.À l'ensemble des professeurs du département de génie électrique et de génie informatique de l'Université du Québec à Trois-Rivières pour l'attention et les efforts portés sur notre formation.À mon frère Mickaël pour son amour fraternel et ses conseils.À mon oncle Drissa pour ses conseils et son aide durant mon séjour d'étude au Canada À mes amis Vivaldi, Rasmané, Akim, Brice, Simon, Coretta et tant d'autres pour leur soutien, conseils et moments passés ensemble.À ma très précieuse amie Guillène pour son soutien et ses conseils.À toutes personnes ayant contribué de près ou de loin à l'accomplissement de ce travail.Pour terminer, je dédie ce travail à la mémoire de ma grande mère maternelle.Annexe C -Contrat intelligent de l'oracle (.sol) .........................
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | medium |
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".