Estimating the Demand, Cost, and Emissions of a Hydrogen-Based Decarbonization Strategy for Airports
Bibliographic record
Abstract
To avert the worst consequences of climate change, the world must transition to a deeply decarbonized energy system over the coming several decades.Some sectors of the energy system will be more difficult to decarbonize than others due to their demands for high energy and power density; among these is aviation, which will likely continue to need liquid or gaseous fuels.This has stimulated research into alternative low-carbon fuel pathways to serve this critical sub-sector of the transportation system.This thesis envisions what Canadian airports might look like in the future if hydrogen is widely adopted throughout the aviation industry.Chapter 1 outlines the research objectives Kouman, Nader Fakrhy, Arthur Banga and Amélie Clauser.Thank you for being believing in me and supporting me.I can only aspire to emulate one day your intelligence and the goodness of your nature.I hope you feel proud of me and my growth,, but above all that you know that my gratitude and affection for you know no bounds.Finally, I would like dedicate this last paragraph to my mother, Solange Catcha-Picard.Being raised by such a caring and intelligent person has been nothing short of a gift.I am incredibly proud to be your child, and I hope you are likewise to be my mom.This is as much your accomplishment as it is mine.Mom, I love you.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".