{"id":"W3110680062","doi":"10.21203/rs.3.rs-25818/v1","title":"The impact of the social distancing policy on COVID-19 new cases in Iran: insights from an interrupted time series analysis","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Social distance; Outbreak; Coronavirus disease 2019 (COVID-19); Distancing; Interrupted Time Series Analysis; China; Medicine; Value (mathematics); Demography; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Disease; Development economics; Political science; Infectious disease (medical specialty); Virology; Sociology; Economics; Internal medicine; Statistics; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005746864,0.0003198464,0.0005138865,0.001186681,0.0002712172,0.00109244,0.0008168007,0.0006857884,0.001481523],"category_scores_gemma":[0.01656027,0.0001695082,0.0008265824,0.001373315,0.0005290502,0.0006415408,0.0005246226,0.001095515,0.0001704861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001054849,"about_ca_system_score_gemma":0.0006644211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01428001,"about_ca_topic_score_gemma":0.008051287,"domain_scores_codex":[0.9967839,0.001795314,0.000228613,0.0003473176,0.000522809,0.0003220408],"domain_scores_gemma":[0.9839369,0.01009742,0.003865257,0.0005386748,0.001153707,0.0004081244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009108502,0.000956616,0.9308748,0.0003377021,0.0007866316,0.001330846,0.001862952,0.03338034,0.0008042242,0.004528495,0.002257872,0.02196873],"study_design_scores_gemma":[0.00004933697,0.001010876,0.8174981,0.00009507655,0.0003403592,0.0002695702,0.004358199,0.1714398,0.0006790225,0.002017582,0.002170281,0.00007175242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954624,0.0003671271,0.001830602,0.0003778206,0.00003740995,0.00004841872,0.000802359,0.00001549132,0.001058392],"genre_scores_gemma":[0.9987503,0.0001174684,0.0004379997,0.00001983425,0.00001910133,0.00002752528,0.0004315969,0.000002480898,0.0001937166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01428001,"threshold_uncertainty_score":0.03039271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5420161461046891,"score_gpt":0.6069656910867625,"score_spread":0.06494954498207339,"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."}}