{"id":"W4238359992","doi":"10.1109/bigdatase53435.2021.00011","title":"Spatial-Temporal Data Science of COVID-19 Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":18,"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; Spatial analysis; Variety (cybernetics); Spatial epidemiology; Coronavirus disease 2019 (COVID-19); Pandemic; Disease; Data mining; Data type; Epidemiology; Geography; Infectious disease (medical specialty); Artificial intelligence; Medicine; Remote sensing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008573662,0.0009927583,0.001148545,0.008131575,0.001518251,0.006389595,0.002586718,0.001444867,0.002423943],"category_scores_gemma":[0.03220906,0.0007517815,0.002690172,0.01198316,0.001809335,0.00862389,0.005062967,0.002495831,0.001871029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767914,"about_ca_system_score_gemma":0.004758475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007860886,"about_ca_topic_score_gemma":0.007296365,"domain_scores_codex":[0.9908949,0.001519955,0.001157625,0.001996601,0.004116463,0.0003144753],"domain_scores_gemma":[0.9786709,0.007343724,0.002249494,0.005485402,0.005594382,0.0006561811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005788738,0.0004392499,0.1346883,0.002684063,0.000984469,0.002181638,0.002831762,0.08651649,0.009925982,0.3113848,0.07780728,0.3699771],"study_design_scores_gemma":[0.00006068606,0.0001675613,0.01733312,0.000549728,0.0002136752,0.001386955,0.002270702,0.4479069,0.01693578,0.2540075,0.2590088,0.0001585867],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02905156,0.002754199,0.8981508,0.007735133,0.0007044628,0.001206244,0.03460906,0.01108823,0.01470033],"genre_scores_gemma":[0.2255947,0.002920165,0.7205085,0.001998823,0.0005931968,0.0006261142,0.04342867,0.0008654548,0.003464401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008573662,"threshold_uncertainty_score":0.04534245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1369181643115538,"score_gpt":0.4036602204499818,"score_spread":0.266742056138428,"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."}}