{"id":"W3038910303","doi":"10.1016/j.healthplace.2020.102389","title":"The need for GIScience in mapping COVID-19","year":2020,"lang":"en","type":"article","venue":"Health & Place","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":94,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Data science; Coronavirus disease 2019 (COVID-19); Big data; Computer science; Disease surveillance; Tracking (education); Health care; Disease; Medicine; Political science; Data mining; Psychology; Infectious disease (medical specialty)","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":[0.0009612315,0.00008241623,0.0002081976,0.00003378803,0.0002087382,0.00001869897,0.0001693505,0.00002492292,0.00002004083],"category_scores_gemma":[0.002571302,0.00006175557,0.00003600022,0.0003895048,0.00007286255,0.00004832774,0.0000503564,0.0001336142,0.00004492106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002197916,"about_ca_system_score_gemma":0.001521901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001854711,"about_ca_topic_score_gemma":0.0002662078,"domain_scores_codex":[0.9987717,0.000089067,0.0002691261,0.000270579,0.0002076533,0.000391855],"domain_scores_gemma":[0.9984252,0.0005383083,0.00009539371,0.0002525112,0.0000308775,0.0006576487],"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":[0.002463188,0.000145321,0.07755604,0.003557666,0.0000266994,0.0001006665,0.008828376,0.0005307817,0.0005347048,0.002666387,0.8801551,0.02343501],"study_design_scores_gemma":[0.001723395,0.000163417,0.01900899,0.00005810712,0.000002457087,0.000008566471,0.001036606,0.009577644,0.000007513399,0.00006265275,0.9682717,0.00007895633],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0622342,0.003774264,0.01289998,0.9156109,0.0003540678,0.003347712,0.0003034318,0.0003039636,0.001171475],"genre_scores_gemma":[0.7298146,0.0004589811,0.003922023,0.264322,0.0003309769,0.0002039961,0.0001382924,0.00003364052,0.0007754422],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.6675805,"threshold_uncertainty_score":0.3078275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09696465147541643,"score_gpt":0.3852968207297224,"score_spread":0.2883321692543059,"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."}}