{"id":"W4229983003","doi":"10.2196/preprints.31813","title":"Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Per capita; Socioeconomic status; Residence; Population; Demography; Geography; Cluster (spacecraft); Geographic mobility; Demographic economics; Sociology; Economics","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":[0.002467579,0.0002794557,0.0006650704,0.0001031856,0.001287201,0.0005046606,0.0003148968,0.0003608829,0.0002060883],"category_scores_gemma":[0.002709832,0.0002166671,0.0002114579,0.00008250661,0.002510895,0.0001732144,0.0005644365,0.0004086709,0.000001331578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002147207,"about_ca_system_score_gemma":0.0007476769,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.137932,"about_ca_topic_score_gemma":0.145918,"domain_scores_codex":[0.9967424,0.001310568,0.0004790726,0.0008663569,0.0002688869,0.0003326768],"domain_scores_gemma":[0.9884686,0.01046265,0.0002521385,0.0004230067,0.000143207,0.000250407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005877094,0.000106203,0.9113353,0.001162671,0.0002685142,0.00001134012,0.07477605,0.00001559283,0.00006326188,0.004496388,0.00002835108,0.007677544],"study_design_scores_gemma":[0.0003436611,0.00006437406,0.8506618,0.0004502437,0.0004305093,0.000003385365,0.1035651,0.001023221,0.001064192,0.04177979,0.00008646512,0.0005272638],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947704,0.00220515,0.0003836601,0.001747923,0.00009925842,0.0006045584,0.00006976107,0.00003869048,0.00008059671],"genre_scores_gemma":[0.9978158,0.001219108,0.00005110667,0.000633625,0.0001521026,0.00008292812,0.00001007984,0.0000100079,0.00002527924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06067349,"threshold_uncertainty_score":0.9900237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05312295761557417,"score_gpt":0.3565001742117014,"score_spread":0.3033772165961272,"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."}}