{"id":"W4282033852","doi":"10.5194/isprs-archives-xliii-b4-2022-111-2022","title":"SPATIAL MODELLING OF COVID-19 INCIDENCE RATE IN CANADA","year":2022,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ordinary least squares; Coronavirus disease 2019 (COVID-19); Pandemic; Geography; Econometrics; Incidence (geometry); Regression analysis; Outbreak; Unemployment; Statistics; Epidemiology; Spatial variability; Spatial analysis; Lag; Spatial epidemiology; Demography; Environmental health; Disease; Medicine; Mathematics; Economic growth; Computer science; Economics; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009437438,0.0004147081,0.0003839773,0.001345464,0.0007498905,0.001160877,0.001203951,0.0003523362,0.002094586],"category_scores_gemma":[0.003114966,0.0002576802,0.0008828031,0.002163664,0.0004763419,0.0003688917,0.0006345816,0.0006034908,0.0002687023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01173225,"about_ca_system_score_gemma":0.013452,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9781892,"about_ca_topic_score_gemma":0.9633064,"domain_scores_codex":[0.9995238,0.00008977616,0.00002318734,0.0001224461,0.00008984853,0.0001509106],"domain_scores_gemma":[0.9986754,0.0002828281,0.0001664872,0.00007048532,0.0007068493,0.00009801907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001362464,0.00005291491,0.7258538,0.0001089835,0.0002036027,0.0003345606,0.0004437101,0.2430952,0.0005657473,0.004958077,0.004098583,0.02014855],"study_design_scores_gemma":[0.00001590956,0.00003001887,0.2150153,0.00006524351,0.00009604179,0.00009847193,0.001161752,0.777955,0.0004804146,0.001096326,0.003935825,0.00004965477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780824,0.0006359336,0.009374589,0.0008850506,0.0000374729,0.00005792492,0.006972854,0.0002219625,0.003731902],"genre_scores_gemma":[0.9928761,0.0002393067,0.002514042,0.0000274554,0.000004952543,0.00001488086,0.002013441,0.00002504467,0.002284789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02181083,"threshold_uncertainty_score":0.08512384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08562021725365646,"score_gpt":0.3240930305411044,"score_spread":0.2384728132874479,"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."}}