Bibliographic record
Abstract
Integrating photovoltaic systems into the urban landscape is fundamental to the wide scale acceptance of the technology. It is also one of the major factors currently limiting its popularity, coupled with high costs, and the lack of adequate storage methods. Contributing to the lack of architectural integration are shortcomings in design tools and an overall lack of forward thinking proposals suggesting how society can begin implementing solar power on a large scale. Research conducted by the International Energy Agency 'Task 41: Solar Energy and Architecture' has identified the need for more developed toolsets to help designers quantify solar exposure and shading coefficients during the conceptual design phase. Although software to calculate incident surface radiation is available, it is largely detached from the traditional design process and workflow. What is required to improve the architectural integration of photovoltaic systems is a new design methodology. A method that must be both inherently flexible and quantifiable, so that designers can validate and modify designs quickly and efficiently. In a process where innovative digital tools combined with the intuition of the designer expand the creative possibilities of intelligent solar architecture. The aim of the project is to develop a new design methodology by combining parametric and environmental analysis tools, providing quantitative performance indicators in order to assist architects at the early design stage. Using case studies, the project will demonstrate how this methodology is applicable to a wide array of project typologies within an urban context. In addition to demonstrating the applicability of the system, the case studies would also illustrate the potential for photvoltaic installations to alter the landscape of the city and facilitate a fresh dialogue between public space and renewable energy generation.
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 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 | 0.001 |
| 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".