Resilience Ruler for Energy Efficiency: An Evaluation of Social Housing Refurbishment
Bibliographic record
Abstract
This study evaluated refurbishment focusing on energy efficiency to improve the resilience of the built environment of social housing from five and ten years ago.Both residential complexes studied were built by the Brazilian social housing program "Minha Casa Minha Vida" ("My House, My Life" in English) in the city of Uberlândia, Minas Gerais state, Brazil.Design science research (DSR) and post-occupancy evaluation (POE) were applied to the energy audit process for built environments.This work aimed to study energy efficiency, behavior, and resilience in the built environment; to introduce the concepts related to the development of indicators and a questionnaire on energy efficiency; and to develop the Resilience Ruler (RR) for energy efficiency and its application in two social housing complexes located in Uberlâ ndia.This paper presents a methodology for energy efficiency resilience assessment (RR) and its result.The RR was developed to measure the resilience level in the built environment based on energy efficiency indicators.The main results indicated that the materialities in the refurbishment and house maintenance received the worst assessment.The roof and the walls materials aren't compatible with the local climate and the residents usually don't have maintenance habits.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".