{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.01309424,0.0009677685,0.00258846,0.002201602,0.002882784,0.0006662674,0.004787579,0.0008940613,0.0005256808],"category_scores_gemma":[0.2012203,0.0005362152,0.001958975,0.009275104,0.002978819,0.0002467852,0.008934738,0.007706114,0.00008150218],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.007946741,"about_ca_system_score_gemma":0.008070602,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2245992,"about_ca_topic_score_gemma":0.06393089,"domain_scores_codex":[0.9661539,0.02180836,0.001950808,0.002119284,0.005453771,0.00251387],"domain_scores_gemma":[0.9185006,0.07461572,0.0006993591,0.003278458,0.0014848,0.001421073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.02646561,0.006451115,0.2466893,0.008695371,0.02225695,0.002315179,0.217891,0.02300924,0.002978226,0.1392779,0.2889238,0.01504614],"study_design_scores_gemma":[0.001486569,0.002484243,0.216927,0.001108601,0.0002086948,0.000002854343,0.01148646,0.006818525,0.0002178842,0.7542655,0.003965246,0.00102844],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8985466,0.001892456,0.0008033218,0.08791495,0.0001243694,0.006494424,0.002478221,0.0003324241,0.001413255],"genre_scores_gemma":[0.9964448,0.0006869388,0.0001462104,0.000224501,0.001184686,0.0004778045,0.0002289647,0.0001207273,0.0004853536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6149876,"threshold_uncertainty_score":0.9997345,"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."}}