DYNAMIC, ADAPTIVE BUILDING ENVELOPES (Hathaway)
Bibliographic record
Abstract
Buildings currently account for 75% of the carbon emissions on the planet, so if we are serious about reducing greenhouse-effects we need to explore better ways to design and construct buildings to achieve improved energy performance. With buildings consuming circa 50% of the United States’ total energy, we should re-examine past practice; building envelopes can no longer be passive, they must become dynamic and adaptive. The development of adaptive buildings requires early design collaboration to examine trade-offs versus energy costs for heating and cooling. Achieving optimized building envelopes requires design to be integrated across disciplines. Selection of curtain wall, glass substrates, solar shading devices, fixed/operable windows, and window sizes requires critical analysis relative to the building’s global site positioning and solar orientation, as well as weather, wind, and context within its built environment. We can develop building envelopes designed to accept or reject free energy from the external ecosystem, and as a result, reduce the cost of power required to achieve a comfortable, internal environment. Case studies such as “The Bow”, Encana’s headquarters in Calgary; Aura, Toronto’s tallest 78-story condo tower, now under construction; and DFR 57, a residential rental project in New York City, will be featured to demonstrate how with collaboration and technology, the AEC industry can bring intelligence to performance-based design and achieve more energy efficient buildings.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.018 |
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".