Data Visualization for a Circular Economy: Designing a Web Application for Sustainable Housing
Bibliographic record
Abstract
An impediment to effective Circular Economy (CE) implementation in residential buildings is the lack of standardized building data to represent a building’s life cycle, from material sourcing to end-of-use apparatus. This paper presents an overarching methodological approach for creating a circular web application named Data Homebase (DHB). DHB integrates housing data into Housing Passports (HPs), visualizing calculations of estimated energy use, carbon emissions, and affordability building indexes. Using data-driven narratives, DHB outlines a building’s degree of circularity and potential for improved environmental outcomes via circular strategies. The passport system and data-based approach provide a once-missing portal entry for housing stakeholders seeking actionable circularity measurements. This research contributes to the long-term elucidation of key decision-making processes for homebuilding within a comprehensive tool to achieve a far-reaching CE.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".