www.trca.on.ca To Our Partners, The development of Partners in Project Green:
Bibliographic record
Abstract
over a decade of partnership between the Greater Toronto Airports Authority (GTAA) and the Toronto and Region Conservation Authority (TRCA). This partnership began with a mutual understanding and a drive to restore, protect and enhance the region’s natural resources. The founding of conservation authorities in the late 1940s was based on the need to protect and promote Ontario’s resources, but with a clear focus on its related resource economy. The TRCA continues to work with industry leaders to promote green technology adoption as a way to reduce threats to the region’s resources while building a sustainable economy. Meanwhile, Canada’s largest employment area has grown not only around, but as a direct result of, Toronto Pearson International Airport generating a signicant economic benet to the region. Along with the increase in business development, the area’s residential communities have also grown. Recognizing the impact of the airport’s operations, the GTAA promotes environmental stewardship within the communities it serves as a longstanding core mandate. Today, it is with the goal of a sustainable green economy that the GTAA and TRCA launch Partners in Project Green. The project’s vision is to work with local businesses in transforming the lands surrounding Toronto Pearson into an internationally recognized eco-business zone. Our goal is to have the companies recognized globally as the greenest in their sectors, and have the area itself become the rst place progressive green-tech companies look to locate. We invite you to take this journey with us and become a partner in Project Green. Work with us to create value for your business, your industry and your community. Together we can make the Greater Toronto Area the greenest place globally to do business.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.754 | 0.434 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".