{"id":"W4372046395","doi":"10.1109/tmbmc.2023.3273193","title":"A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Molecular Biological and Multi-Scale Communications","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung","keywords":"Turbulence; Reynolds number; Computational fluid dynamics; Statistical physics; Probability distribution; Dispersion (optics); Weibull distribution; Physics; Channel (broadcasting); Mechanics; Mathematics; Simulation; Computer science; Statistics; Telecommunications; Optics","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":[0.0003829095,0.0001945518,0.0002342623,0.0001828591,0.0002653506,0.00002635974,0.0006619404,0.0001873444,0.000007450495],"category_scores_gemma":[0.000007639755,0.0001576399,0.0001484432,0.000615514,0.0002299756,0.00005187508,0.00001792755,0.0003245886,0.000003566212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002854619,"about_ca_system_score_gemma":0.00001486712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007512283,"about_ca_topic_score_gemma":0.000004690979,"domain_scores_codex":[0.998716,0.0002885044,0.0004437764,0.0002125152,0.0001274365,0.000211779],"domain_scores_gemma":[0.9984891,0.0003086107,0.00007117953,0.0009672919,0.00009251829,0.00007128777],"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":[0.00004439366,0.0004279087,0.00001567284,0.00004429874,0.00008330066,8.115817e-7,0.0005198844,0.6513109,0.2992155,0.0003613869,0.00000971051,0.04796629],"study_design_scores_gemma":[0.0007723372,0.00005692435,0.00093487,0.00005163101,0.00003547593,0.000004764534,0.00009138202,0.9855184,0.01135588,0.0001978346,0.0007892615,0.000191242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.034819,0.001316513,0.9615622,0.001064909,0.00003323774,0.0009534801,0.00007410144,0.0001328561,0.00004369231],"genre_scores_gemma":[0.9512721,0.006107279,0.04109423,0.0001199631,0.000003453623,0.0006974252,0.0006609152,0.00002838473,0.00001620203],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.920468,"threshold_uncertainty_score":0.642837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03804683076433875,"score_gpt":0.2596153259619249,"score_spread":0.2215684951975862,"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."}}