Adaptation as a Framework for Reconsidering High-Performance Residential Design: A Case Study
Bibliographic record
Abstract
This paper outlines an approach to adaptive residential design explored through recent research and an executed prototype, the North House project (2007-2009), undertaken through an interdisciplinary collaboration of researchers and students from the University of Waterloo, Ryerson University and Simon Fraser University in concert with professional and industry partners. This project aimed to develop a framework for the delivery of adaptive detached residential buildings capable of net-zero energy performance in the temperate climate zone, or the near north. Within this project, the term “adaptive” is developed across several tracts of conceptualization and execution including site and climatically derived models for building material composition and envelope ratios, environmentally-responsive kinetic envelope components, intelligent HVAC controls and interactive interface design aimed at producing co-evolutionary behaviors between building systems and inhabitants. A provisional definition of adaptive architecture is outlined to address this range of considerations that calls into question the stable image of domestic architecture and its relationship to energy and contemporary assumptions regarding sustainable design. This paper also outlines computational approaches to design optimization, distributed building systems integration and the human-controls interfaces applicable to the home’s ecology of physical and information technologies.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".