Biophilic, photobiological and energy-efficient design framework of adaptive building façades for Northern Canada
Bibliographic record
Abstract
This paper develops an integrated design framework of adaptive building façades (ABFs) to respond to photobiological and thermal needs of occupants, biophilic factors, energy requirements and climatic features in Northern Canada, i.e. near and above 50°N. The paper discusses the importance of biophilic and photobiological factors and ABFs to improve occupants’ health and human–nature relations and deal with the extreme climate in Northern Canada where non-adapted buildings that could negatively affect occupants’ well-being. The paper shows that existing ABFs must be further developed for northern applications in terms of (i) the physical structure and configuration of components, (ii) the design of solar shading/louver panels to address photobiological and biophilic requirements, (iii) the development of lighting adaptation scenarios to respond to biophilic and photobiological needs, local photoperiods and energy issues and (iv) the overall biophilic quality for accessibility to natural patterns. The ABFs’ framework was developed in three phases including: (1) process environmental data, (2) produce adaptation scenarios and (3) operate adaptation scenarios. The research discussed major issues of all phases that must be further studied, especially the development of hourly/daily/seasonally lighting adaptation scenarios. The paper develops a holistic parametric methodology to integrate and optimize major design variables of ABF’s components.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".