Bibliographic record
Abstract
This paper explores the role of hydrogen in helping Canada meet its net-zero emissions goals. On the supply side, we caution against the blanket categorization of production methods by “colours”, and instead encourage a focus on the metrics that matter: cost and lifecycle emissions per kilogram. Hydrogen derived from methane, with sequestered carbon dioxide (i.e. “blue” hydrogen) will likely be the method of choice for some time in western Canada, while hydrogen via electrolysis (i.e. “green”) will likely take off in Québec, spreading to other provinces as clean renewable power costs fall. High levels of sequestration (i.e. 90%+) and upstream methane leakage prevention will be essential for hydrogen to meaningfully contribute to Canada’s net-zero goals. On the demand side, we find while hydrogen can do many things, its highest value will be in areas where alternatives for decarbonization are costly or scarce, such as steel and chemicals production, as well as potentially rail and heavy freight. We take a deeper dive into the potential for hydrogen in the electricity system, both by absorbing excess generation via electrolysis, and providing much needed reliability as a peaking product, enabling higher shares of variable renewable energy. We find that by 2030, hydrogen has the potential to compete with natural gas as a dominant firm power source.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".