A Sustainability Based Framework for Evaluating the Heritage Buildings
Bibliographic record
Abstract
There are large number of heritage buildings across the world. Heritage buildings are historically unique by nature and require specific attention to their architecture. Current trends of protection and use of heritage buildings and cultural heritage components testifies to an increasing attention of the study of heritage and legacy. The literature review indicates that there many existing rating systems developed to evaluate the performance of buildings from a sustainability point of view. They all based on three pillars; environment, physical, and society. Also, LEED, BREEAM, CASBEE, ITACA, and others are examples of these rating systems. However, each of them has its own assessment attributes that originate from its local context. Besides, none of the rating systems proposes a definitive guideline for the decision makers to select the best affordable rehabilitation alternatives, taking into account the sustainability of the buildings. Nevertheless, there is an absence of a comprehensive rating systems that could assess heritage building elements and assist facility managers in their rehabilitation decisions. Therefore, the main objective is to develop a comprehensive rating system for heritage buildings that not only evaluates the different building components but also optimizes the expenditures through effective utilization and the allocation of the limited budget among the building 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".