{"id":"W4367860480","doi":"10.48550/arxiv.2305.01412","title":"A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung; Deutscher Akademischer Austauschdienst","keywords":"Turbulence; Reynolds number; Statistical physics; Computational fluid dynamics; Probability distribution; Mechanics; Dispersion (optics); Weibull distribution; Airborne transmission; Transmission (telecommunications); Physics; Molecular communication; Channel (broadcasting); Mathematics; Computer science; Telecommunications; Statistics; Coronavirus disease 2019 (COVID-19); Optics; Infectious disease (medical specialty); Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000276585,0.0004015713,0.0003439311,0.0006037565,0.0004372825,0.000588861,0.000443079,0.0006186238,0.0006708861],"category_scores_gemma":[0.001261865,0.0001818171,0.0004717523,0.0003770945,0.0006016779,0.0007042573,0.0003962425,0.0004484537,0.0001068663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004301682,"about_ca_system_score_gemma":0.000898989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002512763,"about_ca_topic_score_gemma":0.001316208,"domain_scores_codex":[0.9998634,0.00003700238,0.000007913337,0.00002528725,0.00004364606,0.00002280247],"domain_scores_gemma":[0.9994689,0.0002936879,0.0000840607,0.00005839542,0.00006877599,0.00002619257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003001368,0.00005046639,0.002136999,0.00002973116,0.00001686207,0.00006805934,0.0000335387,0.9622138,0.00724509,0.02244881,0.0001649761,0.005561584],"study_design_scores_gemma":[0.000001282823,0.00000549166,0.0001339357,8.767955e-7,0.000001005343,0.000007162024,0.000003567386,0.9981754,0.0003214721,0.001264685,0.00008274784,0.000002327529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1180042,0.0001525386,0.8781034,0.0001584067,0.0000570467,0.00006038402,0.0001758695,0.0001861948,0.003101981],"genre_scores_gemma":[0.8788868,0.0002040365,0.1191732,0.00005594439,0.00004074192,0.0001494361,0.0001873453,0.00004114999,0.001261338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002512763,"threshold_uncertainty_score":0.00499624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05395126363866928,"score_gpt":0.1848025332742683,"score_spread":0.130851269635599,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}