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Record W3193325052

Azilda home retrofit centre: a catalyst for a carbon neutral future in Northeastern Ontario commuter communities

2021· dissertation· en· W3193325052 on OpenAlexaboutno aff
Michelle McLaren

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon fibersGeographyEnvironmental scienceEngineeringMaterials science
DOInot available

Abstract

fetched live from OpenAlex

This thesis proposes a system-based approach to creating affordable and sustainable home retrofits in commuter communities in Northeastern Ontario, more specifically in the community of Azilda. Architecture 2030 has stated that “The urban built environment is responsible for 75% of annual global greenhouse gas (GHG) emissions: buildings alone amount for 39%.” Architecture must play an active role in reducing GHG emissions or the climate crisis will worsen. Addressing climate change is a worldwide effort and is much more attainable through specific municipal level initiatives, or programs. The Home Retrofit Centre proposed within this thesis is a small part of a larger system working towards mitigating further climate change, by focusing on lowering the carbon emissions of residential buildings in Azilda. With linkage to students and professors from the McEwen School of Architecture and the City of Greater Sudbury, the Centre will provide locals with resources on how to improve the efficiency of their homes. In The Philosophy of Sustainable Design, Jason McLennan defines sustainable design as “the philosophy that seeks to maximize the quality of the built environment, while minimizing or eliminating negative impacts to the natural environment.” Retrofitting existing homes will improve the built environment while also reducing the house’s carbon emissions, which will minimize the negative impacts they have had on the environment. A significant part of reducing carbon emissions from buildings relates to embodied and operational carbon, which is produced throughout the entire life cycle of a building. To lower the embodied and operational carbon of the existing homes in Azilda, the Home Retrofit Centre will allow older homes to take on a new life and become more efficient. These retrofits will create a more comfortable and healthy home, update the style, save on energy costs, and more importantly, take action in response to climate change. Retrofitting a home also increases its economic resale value making these changes an investment for the future. For this Home Retrofit Centre to be a success it will need to gently nudge the community in this cultural shift of wanting to improve their homes in a way that will also help with climate change. Most homes will eventually require retrofits or renovations, that is inevitable. This thesis will investigate how the architecture of the Home Retrofit Centre can be a positive catalyst to address the needs of the community and climate change. It will endeavour to answer these questions: How can architecture be a catalyst for these retrofits? And can the Home Retrofit Centre itself showcase and inspire community members to implement retrofit strategies in their own homes in order to guide Azilda into a carbon-neutral future? Beyond simply being a Home Retrofit Centre, it will also become a hub for community activities and resources through the use of cross programming, including a Café, Re-use Store, market area, workshops, and multi-use spaces.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0150.004
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.012
GPT teacher head0.225
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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