MétaCan
Menu
Back to cohort
Record W4321359966 · doi:10.24200/sci.2022.59351.6192

Effect of turbulent and laminar flow mechanisms on airflow patterns and CO2 distribution in an operating room: a numerical analysis

2022· article· en· W4321359966 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientia Iranica · 2022
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAirflowLaminar flowTurbulenceInletEnvironmental scienceFlow (mathematics)MechanicsRoom air distributionAir quality indexMeteorologyMedicineMarine engineeringMaterials scienceSimulationMechanical engineeringComputer sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

Considering the risk of infection in surgeries, maintaining an acceptable indoor air quality in the operating rooms (ORs) to ensure the health and safety of patients and surgical team is very essential. Since airflow is one of the primary mechanisms for transmitting of infections and pollution, it is crucial to examine the air distribution systems in the ORs. In the present study the effect of turbulent and laminar airflow (TAF/ LAF) systems on the air and CO2 distribution in an OR was examined. The effects of inlet and outlet configurations were evaluated for seven different models. The results indicated that the LAF systems is superior over TAF systems. Based on the findings, the LAF with the air curtain configuration brings about the minimum CO2 concentration level in the OR. The results showed that LAF with the air curtain model is able to decrease the CO2 concentration by about 64.66% and 88.96% on central plane, which passes along the body patient on 1.14 m and 1.7 m above the floor, respectively compared to the existing model.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.264
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it