{"id":"W3198857009","doi":"","title":"Multimodal Detection of COVID-19 Symptoms using Deep Learning & Probability-based Weighting of Modes.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Weighting; Coronavirus disease 2019 (COVID-19); Deep learning; Convolutional neural network; Artificial intelligence; Function (biology); Computer science; A-weighting; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Machine learning; Pandemic; Medicine; Biology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000716557,0.001314525,0.0006761847,0.001922088,0.0002310606,0.0005997854,0.0007639872,0.00107284,0.001566485],"category_scores_gemma":[0.002742396,0.0002158012,0.0007863726,0.0008200973,0.0002385135,0.0008667281,0.000922156,0.00123033,0.0008990154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005721347,"about_ca_system_score_gemma":0.0005355742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006170384,"about_ca_topic_score_gemma":0.008860544,"domain_scores_codex":[0.9995922,0.00008435248,0.00003236231,0.0001260978,0.00007211976,0.00009276198],"domain_scores_gemma":[0.9993332,0.0002825941,0.00009708636,0.00005671012,0.0001449418,0.0000853947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001456208,0.0009254295,0.1279143,0.0004539431,0.0004684663,0.0008797777,0.0002177991,0.06965119,0.02783369,0.00174254,0.02831806,0.7401386],"study_design_scores_gemma":[0.00003528262,0.0002305695,0.01987899,0.00005992041,0.00007966773,0.0004570785,0.00008431033,0.9655347,0.006411851,0.004080645,0.003114233,0.00003268852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4694537,0.006893957,0.4919204,0.003098154,0.0007562997,0.0004225482,0.01227272,0.005890498,0.009291646],"genre_scores_gemma":[0.9125071,0.0008756587,0.07376023,0.0005508114,0.000257882,0.0001104959,0.007828557,0.0000824243,0.004026939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006170384,"threshold_uncertainty_score":0.01226896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08584956887251129,"score_gpt":0.2494399312227251,"score_spread":0.1635903623502138,"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."}}