Solar Architecture and Energy Engineering
Bibliographic record
Abstract
Abstract The modern built environment has been developed in a context of readily-available, low-cost energy from highly concentrated fossil fuels. Today's global energy landscape has dramatically changed; energy costs have become significant in the operation of buildings, and the sector uses a major portion of the global resources of fossil fuels. In recent years a major focus of green building development in North America and internationally has been on setting up sustainable energy practices for the built environment. This focus has advanced energy conservation and efficiency measures for buildings; on-site clean energy generation is now positioned as a critical next step in meeting increasing energy demands while enhancing the functionality and comfort of buildings. “Solar Architecture” as a green building concept addresses sustainable energy practices and the needs of the three major tiers of the built environment: community planning, existing buildings, and new construction. This article uses a case study of integrating renewable energy engineering into university campus energy planning to demonstrate some of the roles energy engineering plays in our built environment. As part of a master planning process for Dalhousie University, solar energy generation potential mapping and the SolarStarRating™ system were used to facilitate the integration of solar technologies into the community energy mix. The process identified the buildings most suited to retrofitting with solar technologies, and enabled the best opportunities to be investigated.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".