LES BESOINS UNIVERSELS EN APPROVISIONNEMENT ENERGETIQUE
Bibliographic record
Abstract
Nuclear plants emit virtually no greenhouse gases over their full life-cycle. Consequently, continued operation of existing nuclear plants is recognized as essential to meeting even the modest greenhouse gas reduction targets of the Kyoto Accord. However, much expanded nuclear deployment will be needed as developing economies aggressively grow GDP with its associated growth in electrical power. Projecting to 2040 and based on the scenarios of the United Nations Intergovernmental Panel on Climate Change's (IPCC), we have examined deploying increased non-carbon energy sources for electricity production, including further conversion of electricity to hydrogen using conventional low-temperature water electrolysis. Our NuWind © model has been used to calculate the production costs for hydrogen in typical potential markets, using the actual prices of electricity paid by the Alberta Power Pool and by the Ontario Grid. The analysis shows clearly that by optimizing the co-production of hydrogen and electricity (referred to as the H2/e process) the cost for hydrogen produced can comfortably meet the US Department of Energy's target for realistic nuclear investment costs, hydrogen generation systems, and wind capacity factors. The synergy of nuclear plus wind power for hydrogen generation plus co-production of electricity improves the economics of harnessing wind energy to produce hydrogen.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".