{"id":"W4386738218","doi":"10.1111/cag.12879","title":"Associations between socio‐demographic factors and change in mobility due to COVID‐19 restrictions in Ontario, Canada using geographically weighted regression","year":2023,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Geography; Census; Pandemic; Demographics; Coronavirus disease 2019 (COVID-19); Geographic mobility; Geographically Weighted Regression; Multilevel model; Regression analysis; Demography; Demographic economics; Population; Computer science; Statistics; Sociology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001320299,0.0005962487,0.0005185264,0.001672962,0.001880716,0.001391541,0.001833875,0.0004002303,0.003407274],"category_scores_gemma":[0.005111728,0.0003662223,0.001231579,0.004560066,0.0007433561,0.0005864017,0.001244279,0.0006986024,0.0003875385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03074082,"about_ca_system_score_gemma":0.0409826,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9984319,"about_ca_topic_score_gemma":0.9982462,"domain_scores_codex":[0.9989381,0.0001365368,0.00009871249,0.0002402153,0.0002831818,0.000303219],"domain_scores_gemma":[0.9965668,0.0003500863,0.0006315559,0.0002023605,0.00176664,0.0004825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005837336,0.00002238024,0.9887255,0.00005914978,0.0001955931,0.00008290896,0.000539008,0.00296294,0.00009399045,0.0005191714,0.002923496,0.003817496],"study_design_scores_gemma":[0.00001327817,0.00002178183,0.982924,0.00007546062,0.0001196082,0.00003492498,0.001395758,0.01092408,0.00009124642,0.0001755871,0.00419475,0.00002952545],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.96269,0.0009616387,0.002593071,0.001149459,0.00005929264,0.0001094531,0.02765284,0.0001056837,0.00467861],"genre_scores_gemma":[0.9880654,0.0003980217,0.001217029,0.00008001696,0.000009152679,0.00007624232,0.006768967,0.00002006861,0.00336507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03074082,"threshold_uncertainty_score":0.2230413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03846485783070833,"score_gpt":0.2691143507773096,"score_spread":0.2306494929466012,"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."}}