COVID-19 Distribution Predicting in Nursing Caring Office Room: A Case Study at Princess Maha Chakri Sirindhorn Medical Center
Bibliographic record
Abstract
The risk of spreading the virus largely depends on the airflow behavior and the change in direction caused by the air supply and location of the exhaust air. The generated particles may travel long during sneezing, adversely affecting human bodies to defend against COVID-19 infectious diseases. This paper examines airflow path and airborne pollutant distribution in the nursing caring office room for COVID-19 patients ward at Princess Maha Chakri Sirindhorn Medical Center, Nakhornnayok province by computational fluid dynamics modeling and field measurement. Fifteen dummies of nursing staff stay in the room, and only one dummy (Patient) generated COVID-19. It is found that the generated particles during sneezing may travel a long distance as compared with the normal respiration and the ventilation system can effectively remove contaminants from the room and distribution in the room. From the obtained results, the keeping a social distancing should be more than 1.5 m for preventing the spread of the COVID-19 from person to person.
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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 | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".