Hydrogen Systems: A Canadian Opportunity for Greenhouse Gas Reduction and Economic Growth
Bibliographic record
Abstract
Achieving stabilization in atmospheric carbon dioxide (CO2) levels calls for per capita reductions in excess of 50% from today's levels in the next half century. Hydrogen systems could provide both immediate and long-term emission reductions to achieve this goal. The emission benefits of hydrogen technologies derive not just from the increased efficiency associated with hydrogen-based energy conversion processes such as fuel cells, but also from the consideration of hydrogen as an energy carrier and industrial feedstock within a larger energy system. Because of our abundant energy resources and leadership in hydrogen technologies, Canada is well positioned to lead in the transition to a hydrogen economy. This paper looks at strategies for the transition to hydrogen-based energy systems that were developed through workshops involving 60 representatives from Canadian industry, government and academia. The purpose of these workshops was to: examine near-term technologies; determine whether the development of hydrogen technologies could be accelerated within the time frame for greenhouse gas (GHG) reductions in Canada's Project Green initiative, yielding a "visible" contribution at reasonable cost (> IMT reduction in CO2equivalent emissions); and establish a direction for future energy processes that will address the need for far greater emission reductions in the future.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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, 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".