Electrifying natural gas : a dynamic model of Québec's energy transition
Bibliographic record
Abstract
Dans ce mémoire, j’évalue les implications économiques d’une électrification efficace des usages du gaz naturel. Pour ce faire, je développe un modèle dynamique en équilibre partiel des marchés du gaz naturel et de l’électricité dans lequel la demande est détaillée par service énergétique. Le modèle tient également compte des contraintes de capacité et des coûts d’ajustement du réseau électrique. L’implémentation numérique du modèle, laquelle est calibrée au contexte québécois, révèle que la transition énergétique est guidée par le prix des émissions de GES. Une transition optimale implique que les investissements dans les énergies renouvelables débutent tôt et soient répartis à travers le temps. Par ailleurs, le prix du carbone en place ne mène pas à une électrification qui permette l’atteinte des cibles du gouvernement du Québec.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".