{"id":"W4319230836","doi":"10.1162/2e3983f5.72a97f69","title":"Review 1: \"Characterizing Responsiveness to the COVID-19 Pandemic in the United States and Canada Using Mobility Data\"","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Preprint; Pandemic; Mobile phone; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Phone; Confounding; Population; Geography; Econometrics; Computer science; Demography; Statistics; Medicine; Telecommunications; Mathematics; Virology; Sociology; World Wide Web; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07928994,0.001757632,0.003691406,0.01151413,0.003920543,0.00726665,0.005093643,0.006237824,0.01603739],"category_scores_gemma":[0.4929195,0.001310455,0.002804575,0.01093535,0.004780754,0.003778365,0.002959098,0.004004477,0.007316154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01634839,"about_ca_system_score_gemma":0.07355227,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1702244,"about_ca_topic_score_gemma":0.1979271,"domain_scores_codex":[0.9417794,0.02001736,0.005498433,0.003185919,0.02804643,0.001472376],"domain_scores_gemma":[0.3898571,0.1370618,0.01790154,0.01322817,0.4355078,0.00644345],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006107157,0.000007621161,0.0007071098,0.008419714,0.0002991029,0.00002563583,0.0001472026,0.00009818188,0.00005260387,0.001164783,0.9605855,0.02843147],"study_design_scores_gemma":[0.0001456413,0.00005956718,0.00803697,0.04049611,0.001164999,0.0001300605,0.0004295683,0.0004216217,0.0002834954,0.00280157,0.9458842,0.0001461308],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.001718537,0.2865651,0.004780077,0.3681484,0.3072439,0.002777901,0.01374735,0.0005726185,0.0144461],"genre_scores_gemma":[0.04228554,0.5757846,0.006830213,0.1659742,0.1544421,0.003159552,0.01148864,0.001191015,0.03884427],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9207101,"threshold_uncertainty_score":0.4193303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2240763228364973,"score_gpt":0.4373547885136984,"score_spread":0.2132784656772011,"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."}}