Smart and sustainable building appraisal
Bibliographic record
Abstract
In general terms, energy efficiency and conservation appraisal aspire to deliver an insatiable energy demand with less energy within the most significant amount of conservation and environmental benefits at the lowest possible price. Sustainable planning and design rely on a series of multi-disciplines: technical, technological, social, political, environmental, ecological, economic, institutional, and global restrictions that abstruse viable decision-making. Recent reports indicate that the residential building sector consumes 40% of the total energy and emits 30% of greenhouse gas (GHGs) worldwide. Thus accordingly, energy consumption in buildings is estimated at one-third of total primary energy resources. Therefore, proper modeling and optimization of a sustainable building in terms of energy efficiency and saving become a matter of focus. This paper explores an emerging picture of influential factors in the context of hands-on roadmap for energy-efficient and smart city planners, practitioners, scholars, and researchers. This study reviews the main points and proposes a framework in detail in the upcoming studies. Meanwhile, another objective of this paper was to introduce the most crucial indicators of energy-efficient building planning, design, and optimization to draw an exhaustive roadmap in compliance with resiliency, sustainability, and efficiency criteria throughout the lifecycle of a sustainable building.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".