{"id":"W2953549290","doi":"10.1109/access.2018.2889683","title":"An Experimentally Validated Channel Model for Molecular Communication Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Research Foundation; National Research Foundation of Korea; Ministry of Education","keywords":"Molecular communication; Computer science; Channel (broadcasting); Focus (optics); Noise (video); Communications system; Function (biology); Sensitivity (control systems); Modulation (music); Experimental data; Data modeling; Electronic engineering; Acoustics; Telecommunications; Artificial intelligence; Physics; Optics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000132897,0.0001211462,0.0001399298,0.00006036858,0.00005252815,0.0001490422,0.0008897184,0.0000912032,0.00001010345],"category_scores_gemma":[0.000001847795,0.0001338312,0.00004550786,0.0001129274,0.00001150668,0.0003153501,0.00004537061,0.00009193528,0.00001974448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004021581,"about_ca_system_score_gemma":0.00001030799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001947903,"about_ca_topic_score_gemma":0.000003684303,"domain_scores_codex":[0.9993576,0.00005020336,0.0001915967,0.0001320443,0.0001017701,0.0001667605],"domain_scores_gemma":[0.9987417,0.00002011191,0.00004089008,0.001075773,0.00005980453,0.00006164701],"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":[0.000007998103,0.00002672345,0.00001172316,0.00002901141,0.00002555315,2.212858e-7,0.000167901,0.9040806,0.0941475,0.0008255339,0.0005522667,0.0001249073],"study_design_scores_gemma":[0.000353444,0.00001441002,0.000007323812,0.00002944392,0.000008298051,0.000001119442,0.0000338973,0.9507779,0.04813116,0.0001175809,0.0003624511,0.0001630001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.316012,0.001572863,0.678642,0.0000247863,0.0003055159,0.0007897572,0.00001163754,0.0003310717,0.002310418],"genre_scores_gemma":[0.9978125,0.0001044094,0.001444394,0.0001097388,0.00001656391,0.0002278806,0.000154245,0.00005252378,0.0000777326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6818005,"threshold_uncertainty_score":0.545748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03088093193745705,"score_gpt":0.3043406303976019,"score_spread":0.2734596984601448,"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."}}