Using Blue\nHydrogen to Decarbonize Heavy Oil and Oil\nSands Operations in Canada
Bibliographic record
Abstract
To decarbonize Canada’s heavy oil and oil sands\noperations,\nwe propose replacing natural gas as fuel by hydrogen, which can be\nproduced by steam methane reforming with carbon capture and storage\n(CCS). Results show that using hydrogen as fuel in 162 heavy oil and\noil sands projects in Canada would save 1.5 trillion cubic feet per\nyear of natural gas between 2021 and 2050. This amount of natural\ngas can produce 15.2 million ton per year of blue hydrogen by steam\nmethane reforming, of which only 12.6 million ton per year is needed\nin Canada’s heavy oil and oil sands operations, resulting in\n76% reduction in CO<sub>2</sub> emissions. Furthermore, implementing\nCCS at a capacity of 105 million ton per year is proposed for Central\nAlberta. Using blue hydrogen as fuel in the heavy oil and oil sands\noperations is profitable when Canada’s carbon tax reaches $99\nper ton in 2027.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.057 | 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 teacher head, 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".