A climate positive future: adapting and recovering the Dominion Wheel and Foundry Complex in Toronto through climate positive design
Bibliographic record
Abstract
The impending climate crisis is becoming increasingly concerning. Architecture and the building industry play a large role. More than ever, it is an architect’s responsibility to create sustainable designs in order to mitigate climate change. This calls into question what is considered sustainable. The discourse on sustainability is ongoing and this thesis attempts to expand on these arguments. Sustainable architecture addressed in this thesis deals with the adaptation of existing buildings to reduce a city’s environmental impact. This thesis supports the argument that buildings cannot be sustainable on their own; the entire process and context need to be considered, which means adapting existing buildings and ensuring the potential for further adaptation to changing social and climatic conditions. Architects must work towards climate positive designs to have a significant impact on the climate crisis. This thesis project demonstrates how climate positive design can be achieved through sustainable adaptive reuse. This project proposes to adapt the Dominion Foundry heritage complex in Toronto into a climate positive community hub. Through sustainable architectural and urban design, the project will serve as a model to demonstrate and educate the citizens of Toronto and beyond on sustainable practices and advocate for a safer and healthier 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.022 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".