{"id":"W4387844510","doi":"10.1038/s41597-023-02638-6","title":"Non-pharmaceutical interventions to combat COVID-19 in the Americas described through daily sub-national data","year":2023,"lang":"en","type":"article","venue":"Scientific Data","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Inter-American Development Bank","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Psychological intervention; Pandemic; Geography; Medicine; Virology; Outbreak; Nursing; Internal medicine; Infectious disease (medical specialty)","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","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.01330504,0.0002037958,0.000358554,0.0001879122,0.0006456607,0.0003158528,0.005891856,0.00005715288,0.0002330037],"category_scores_gemma":[0.0553017,0.0001395274,0.00008585605,0.002709989,0.0006784184,0.000516385,0.008506398,0.0003161897,0.001124305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001888472,"about_ca_system_score_gemma":0.0002608043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002285783,"about_ca_topic_score_gemma":0.001294936,"domain_scores_codex":[0.9960117,0.0004020834,0.0007066745,0.001367012,0.0009372781,0.0005752097],"domain_scores_gemma":[0.988589,0.007005747,0.0001525075,0.003944119,0.00010933,0.0001993343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001421722,0.0002354425,0.001553008,0.0001453183,0.00003064224,0.0000235165,0.0007935127,0.00003706436,0.0001418546,0.009429253,0.9869602,0.0006359643],"study_design_scores_gemma":[0.0006866385,0.00004322725,0.01312451,0.0001126484,0.00009908158,0.000009447793,0.001868625,0.01346563,0.00002827287,0.1435777,0.8266193,0.0003650001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1203185,0.0009307844,0.3360111,0.424582,0.007725905,0.008002977,0.09506971,0.001921797,0.005437258],"genre_scores_gemma":[0.8389106,0.0002705679,0.05002365,0.04964719,0.0006899669,0.0006443165,0.05611252,0.0001161602,0.003585042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7185921,"threshold_uncertainty_score":0.9996535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8428128399512812,"score_gpt":0.6118075745123546,"score_spread":0.2310052654389266,"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."}}