{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003037368,0.000176985,0.0002228274,0.0001796462,0.00006476343,0.00001959242,0.000667417,0.0002178198,0.000005557693],"category_scores_gemma":[0.000008002498,0.0001846558,0.0001481577,0.0003892866,0.00005113481,0.00004969131,0.0001842917,0.0003317946,0.000002053511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008095784,"about_ca_system_score_gemma":0.00003585579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001813167,"about_ca_topic_score_gemma":0.000002286875,"domain_scores_codex":[0.9991325,0.0001368004,0.0002614703,0.0002521811,0.00006905741,0.0001479628],"domain_scores_gemma":[0.9989166,0.000116593,0.0001241845,0.0007045225,0.00009748186,0.00004061145],"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":[0.00002650875,0.00004783682,0.00003350269,0.0001637943,0.00007231588,0.000002958514,0.0002443268,0.9938902,0.001639664,0.003182478,0.0000223756,0.0006740552],"study_design_scores_gemma":[0.0004054443,0.000009772824,0.0007816624,0.0001241671,0.00005851249,6.519552e-7,0.00003795894,0.9951216,0.0003402325,0.00270419,0.0002453077,0.0001704751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06236559,0.0003822303,0.9359871,0.0001181882,0.00006515106,0.0008478305,0.00003885094,0.0001165995,0.00007846687],"genre_scores_gemma":[0.9942746,0.001573116,0.002284159,0.00002342304,0.0000125504,0.00002373153,0.001704467,0.00004244424,0.00006147895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9337029,"threshold_uncertainty_score":0.7530043,"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."}}