Retrofitting Suburban Homes for Resiliency: Design Principles
Bibliographic record
Abstract
The housing options in North American suburbs will fail to meet many future housing needs. An aging population, a younger generation who prefer walkable places, economic shifts, and the environmental impacts of suburban development are all contributing factors. Suburban retrofitting is occurring or imminent in many suburbs to stave off obsolescence. Although several models exist for the sustainable development of greenfields or large swaths of land, limited models exist for the retrofitting of existing suburban housing stock. To avoid poorly-designed retrofit schemes, this research proposes retrofit design principles aimed at dividing large suburban homes into multiple housing units. A set of design guidelines and practical examples of how large suburban homes can be divided are presented for use by homeowners (or their contractors) to begin the division of a single-family home. The main goal is to equip homeowners and their contractors with the knowledge to divide homes into independent, well-designed, and livable units. Two example divisions are shown with before and after division floor plans along with an explanation of how the guidelines were applied. This research contributes to a burgeoning paradigm shift in terms of the decision-making process at all levels of regional planning, with the expectation to lead to more innovative designs for housing division projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".