A ROADMAP FOR CLIMATE ACTION AT THE UNIVERSITY OF CALGARY: HIGHER EDUCATION CAMPUSES AS CLIMATE LEADERS
Bibliographic record
Abstract
INTRODUCTION As federal and provincial governments debate the viability of absolute emission reduction targets, universities and college across North America are steadfast on a voluntary movement to slash greenhouse gas emissions and model the way forward on climate action. These institutions are taking advantage of campus contexts that offer decentralized energy supply opportunities, district energy systems, large building portfolios, and research partnerships to leverage change. Higher education campuses are emerging as innovation hubs for the deployment of new technologies, policy development, best practices in portfolio scale building operating models, public-private partnership models and more. Situated in the heart of the corporate oil and gas sector, the University of Calgary is one such innovation hub. To date, the University of Calgary has realized reductions equivalent to 35% of its 2008 main campus baseline emissions and approximately $7.4 million in annual cost avoidance. By 2016, the University of Calgary's 50th anniversary, the institution aims to attain a 45% reduction in emissions. Energy Innovation is one the research platforms supporting the University of Calgary's Eyes High strategy to become one of the top five research institutions in Canada by 2016. Operational innovation in the management of energy and greenhouse gas emissions (GHG) is a corresponding initiative. This article overviews the strategies behind the progress to date within institutional operations.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".