{"id":"W3035560896","doi":"10.1177/2043820620934209","title":"On the relationships between COVID-19 and extended urbanization","year":2020,"lang":"en","type":"article","venue":"Dialogues in Human Geography","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Urbanization; Coronavirus disease 2019 (COVID-19); Pandemic; Outbreak; Economic geography; Infectious disease (medical specialty); 2019-20 coronavirus outbreak; Corporate governance; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Geography; Development economics; Disease; Economic growth; Economics; Biology; Virology; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00117775,0.0001728786,0.0003160454,0.0001224657,0.0004989985,0.00003162501,0.0002155396,0.0001172273,0.00005446662],"category_scores_gemma":[0.02189193,0.0001181627,0.00008933031,0.0005276077,0.0002874133,0.00004979643,0.0001475368,0.0003868342,0.00001215389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003573259,"about_ca_system_score_gemma":0.00001145011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006462413,"about_ca_topic_score_gemma":0.00008129246,"domain_scores_codex":[0.9981999,0.0006502511,0.0003966427,0.0003604986,0.0001746574,0.0002179865],"domain_scores_gemma":[0.9892659,0.01018083,0.0001418074,0.0002359638,0.00003015927,0.000145371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009543862,0.00003444503,0.6567974,0.00005317417,0.00003165222,0.000002718939,0.002215817,0.00002358361,0.000004756573,0.3354573,0.005298079,0.00007153439],"study_design_scores_gemma":[0.0002063844,0.00008826357,0.3677077,0.00001182746,0.0000233373,1.122902e-7,0.0001354106,0.00003658038,0.000003383318,0.6301474,0.001529315,0.0001102856],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618984,0.000405099,0.006647462,0.02759956,0.00005560658,0.0009468721,0.00005546339,0.0003528814,0.002038593],"genre_scores_gemma":[0.9945254,0.00004227826,0.0003871045,0.004794192,0.0001268298,0.00005926556,0.00004377745,0.00001476827,0.000006368953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2946901,"threshold_uncertainty_score":0.9863471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3943212965362517,"score_gpt":0.4020553460804864,"score_spread":0.007734049544234733,"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."}}