Energy Efficiency Indicators and the First Design Stages for Commercial Centers after the Coronavirus Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic, which emerged in the last quarter of 2019, has seriously affected the global economy, including sectors such as the energy and building industries. Studies of COVID-19 transmission indicate a direct relationship between the number of occupants in a building and the risk of infection. The aims of this study were to focus on workplace density strategies as a primary, overlooked factor that can affect energy consumption and the risk of transmission of viruses within buildings and to determine optimal workplace density strategies to reduce energy consumption, especially in commercial buildings. To this end, the practical approach was used by applying COVE.TOOL technology and data from COVID-19 tracking projects to the proposed occupant density after new design considerations for the food court of the Mall of Arabia – the most famous shopping mall in Egypt. This approach was also used to evaluate customer visits to reduce the spread of disease and improve their energy efficiency.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".