Building Occupancy Simulation and Analysis under Virus Scenarios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the COVID-19 pandemic, regulations on building usage and occupancy density were brought to the forefront, as research indicated that transmission was most likely to occur in indoor environments. Public health officials and building managers had to decide how to best use their buildings while curtailing the infection risk for their occupants. In this article, we present a systematic simulation-based methodology for estimating the infection risk for a building’s occupants under different scenarios of building usage. We have evaluated our simulations against some real-world building usage data from a university campus building; our experiments demonstrate the realism of our simulations. Based on this finding, we have developed a virus transmission model that estimates the potential infection transmission risk given the behaviors of a building’s occupants. Our methodology enables building managers to simulate alternative building usage scenarios and estimate their relative infection transmission risk. We argue that such risk estimate comparisons can be useful in making decision about alternative building usage options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it