Bibliographic record
Abstract
The relationship between buildings and image, both static and moving, has occupied a significant role throughout architectural history. Current innovations in materials research, digital technology and lighting are strengthening this connection as a vital idea permeating image arts and built spaces. The objective of this paper is to expand the intersection of architecture and image within the context of climate change. As the recent kinetic art and media facades as well as the author’s own prototypes highlight, this emergent practice promotes climate conscious interactive systems and sustainable illumination that are integrated into building surfaces. While responding to changes in environmental variables and illuminating the building with renewable energy sources, this alternative approach also emphasizes developing a social and environmental narrative for the use of media facades, challenging the current application for mainly commercial advertising. The author’s own climate responsive prototypes reveal the methodological experimentation with weather elements of snow, rain, light and wind. The three-tiered display of collected climactic information lead to integration of various technologies to make the elements visible, harvest and generate energy from their kinetic movement. This paper will present conclusions drawn from the previous experimentation, as well as a new direction to eliminate additional light pollution while integrating architectural and climate visualization with the sustainable media façade technology in built environments.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".