Retrofitting for resiliency and sustainability of households
Bibliographic record
Abstract
Home retrofits contribute to the sustainability of residential buildings by conserving resources and energy and improving efficiency of the operations within. The resiliency of a household to disruption is usually a separate consideration, if at all. The up-front costs of both can present themselves as nonessential expenses limiting their adoption. There exist few tools to integrate design for sustainability and resiliency that are available to average homeowners. This inhibits their ability to implement climate change mitigation and adaptation measures. Herein is a systems approach to integrate sustainability efforts with resilience solutions into a computational multi-objective decision support methodology with a financial analysis. The methodology, dubbed “ReSus”, is shown here with an example case study of a midsize single detached house in southwestern Ontario, Canada through simulation of retrofitting scenarios to support decision making on building upgrades. Applying this methodology details several retrofitting pathways that have the potential to reduce energy use and greenhouse gas emissions as well as provide a positive return on investment that addresses both mitigation and adaptation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".