{"id":"W3157720081","doi":"10.1109/hpcc-smartcity-dss50907.2020.00177","title":"Spatial Data Science of COVID-19 Data","year":2020,"lang":"en","type":"article","venue":"","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Data science; Computer science; Coronavirus disease 2019 (COVID-19); Spatial analysis; Analytics; Spatial epidemiology; Variety (cybernetics); Data analysis; Pandemic; Disease; Epidemiology; Data mining; Infectious disease (medical specialty); Medicine; Geography; Artificial intelligence; Pathology","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007086191,0.00008772021,0.0002214029,0.0000818825,0.00006729447,0.00002379085,0.001872137,0.00003116476,0.0009637733],"category_scores_gemma":[0.0114959,0.00007356566,0.00001514598,0.0006379128,0.0004254327,0.0003940043,0.002836027,0.0001052636,0.00008241147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006517979,"about_ca_system_score_gemma":0.002739404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003001262,"about_ca_topic_score_gemma":0.00015131,"domain_scores_codex":[0.9982265,0.00002048442,0.0002349512,0.0007351591,0.0005914419,0.000191479],"domain_scores_gemma":[0.9961177,0.0002831264,0.0000721547,0.002914533,0.00007987839,0.0005326738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001905076,0.0003525697,0.01936325,0.0006805409,0.00004360142,0.00009961035,0.0006947668,0.00004862376,0.03180457,0.0006978231,0.9372169,0.008807217],"study_design_scores_gemma":[0.00188,0.0003394831,0.004100188,0.00006012728,0.00016024,0.00001699402,0.0001665118,0.1119527,0.01108752,0.00003168543,0.8700157,0.0001888725],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02260107,0.0001875228,0.04177547,0.930934,0.0003183335,0.0006869855,0.0008792317,0.0003256307,0.002291789],"genre_scores_gemma":[0.8474742,0.00003946951,0.006111391,0.1456176,0.0002459412,0.000001385615,0.0004403741,0.00001311087,0.00005654183],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.8248731,"threshold_uncertainty_score":0.9999495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2609284632176634,"score_gpt":0.4376495589182181,"score_spread":0.1767210957005547,"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."}}