{"id":"W3020546315","doi":"10.1101/2020.04.17.20070086","title":"Estimating the impact of COVID-19 control measures using a Bayesian model of physical distancing","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; BC Centre for Disease Control; University of British Columbia; Simon Fraser University; University of Victoria; Fisheries and Oceans Canada","funders":"","keywords":"Social distance; Distancing; Coronavirus disease 2019 (COVID-19); Population; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Bayesian probability; Econometrics; Demography; Psychology; Statistics; Medicine; Environmental health; Mathematics; Disease; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002195057,0.0004795354,0.001928254,0.00005989563,0.0001711232,0.00002087193,0.0007264831,0.0001886905,0.00001019752],"category_scores_gemma":[0.04913011,0.0002755916,0.000998002,0.0001921152,0.0003763906,0.00003243087,0.0008457593,0.0007611561,6.789136e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004271212,"about_ca_system_score_gemma":0.0006035513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001039012,"about_ca_topic_score_gemma":0.00003907717,"domain_scores_codex":[0.996717,0.0006869652,0.001071344,0.0005742592,0.0005621127,0.0003883122],"domain_scores_gemma":[0.989244,0.008183545,0.001388556,0.0007907106,0.0001873134,0.0002058845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001059464,0.0001312182,0.02382881,0.001871313,0.0006155848,0.00000538992,0.004123758,0.9582753,0.00770412,0.003073224,0.000127928,0.000137447],"study_design_scores_gemma":[0.0002653899,0.00004770084,0.0008188831,0.0002072722,0.0002581587,7.615195e-7,0.00005455793,0.7103223,0.0001029526,0.287747,0.000001085896,0.0001738721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4040717,0.0001263563,0.5940506,0.0009226787,0.00003589546,0.0005345886,0.0001364464,0.00006740287,0.00005428541],"genre_scores_gemma":[0.9728586,0.000004253336,0.02665016,0.0002034394,0.0001858736,0.00004835325,0.000002513329,0.00004457757,0.000002197714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5687869,"threshold_uncertainty_score":0.9999696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3504606242974277,"score_gpt":0.4660962304821384,"score_spread":0.1156356061847107,"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."}}