Sustainable and Resilient Architecture: Prioritizing Climate Change Adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this paper is to emphasize the need for architects to prioritize sustainability and resilience in building design, especially in the face of an increase in the frequency and intensity of natural disasters caused by climate change. Sustainable, climate-adapted, and resilient architecture can reduce greenhouse gas emissions, promote resource efficiency, and improve people's quality of life. This paper explores the design aspects of the top ten COTE projects for 2023 recognized by the American Institute of Architects. These projects emphasize sustainable performance, stormwater and energy reduction strategies, and design-for-change principles. The main objective is to identify how sustainable project design adapts to climate change and supports resilient recovery from disasters. The methodology involves identifying and reviewing design criteria for sustainable performance. It also involves analysing stormwater runoff and energy reduction strategies. It investigates the futuristic vision for design for change, and highlights design innovations accomplished through the selected projects. This paper provides valuable insights into how projects approach adaptive and resilient design through sustainability. Architects can benefit from this holistic approach to designing spaces that adapt to the ever-changing climate and promote sustainable design innovation.
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.
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 | 0.000 | 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 | 0.000 | 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 it