{"id":"W4387916168","doi":"10.4236/jgis.2023.155027","title":"A Spatial Epidemiology Case Study of Coronavirus (COVID-19) Disease and Geospatial Technologies","year":2023,"lang":"en","type":"article","venue":"Journal of Geographic Information System","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Geospatial analysis; Geography; Cartography; Spatial epidemiology; Spatial analysis; Geomatics; Demography; Coronavirus disease 2019 (COVID-19); Cluster analysis; Population; Statistics; Epidemiology; Medicine; Remote sensing; Infectious disease (medical specialty); Disease; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006019228,0.0002320283,0.00108028,0.000992281,0.0002832764,0.00001976382,0.0002677386,0.0001687678,0.000006774624],"category_scores_gemma":[0.02301253,0.0001609311,0.0002266449,0.0006667039,0.0002313992,0.0004084597,0.0003027208,0.0003204214,0.000006238646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001162396,"about_ca_system_score_gemma":0.0001119713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467872,"about_ca_topic_score_gemma":0.0001937823,"domain_scores_codex":[0.9959313,0.0006300876,0.002561279,0.0001511703,0.0004033149,0.0003228468],"domain_scores_gemma":[0.9920718,0.004210718,0.002741318,0.0003303174,0.0004151826,0.0002306281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003960823,0.0001335947,0.9786483,0.00172876,0.0004358661,0.0008546029,0.004335022,0.0006455237,0.000001790517,0.003652635,0.0006159829,0.00855183],"study_design_scores_gemma":[0.01002895,0.004386413,0.6216193,0.0006946324,0.001269906,0.006743621,0.2932501,0.01804064,0.000006036845,0.03791045,0.005073977,0.0009759764],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849287,0.0001903648,0.0125747,0.0009594808,0.0002761456,0.000737155,0.00003956579,0.0002731783,0.00002071484],"genre_scores_gemma":[0.9990981,0.0001475276,0.0004635845,0.0001696868,0.00006062575,0.00004761463,0.000002559313,0.000007974769,0.000002393682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.357029,"threshold_uncertainty_score":0.985217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2731079242991284,"score_gpt":0.4363461232136345,"score_spread":0.1632381989145061,"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."}}