{"id":"W3014668173","doi":"10.48550/arxiv.2004.02779","title":"COVID-19: Analytics Of Contagion On Inhomogeneous Random Social Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Population; Social distance; Ordinary differential equation; Social network (sociolinguistics); Econometrics; Limit (mathematics); Social network analysis; Computer science; Coronavirus disease 2019 (COVID-19); Mathematics; Differential equation; Social media; Medicine; Sociology; Demography","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":[0.00291994,0.0006137611,0.0008463886,0.001695126,0.0005047448,0.001596427,0.001615503,0.0009244248,0.002194567],"category_scores_gemma":[0.01761845,0.0003727888,0.0007247954,0.001067264,0.001231846,0.002589458,0.002180233,0.001491137,0.000308566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294864,"about_ca_system_score_gemma":0.0006759455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008886844,"about_ca_topic_score_gemma":0.003735685,"domain_scores_codex":[0.9991148,0.0004475512,0.00004076675,0.0001380412,0.0001589501,0.00009990537],"domain_scores_gemma":[0.9939721,0.003799297,0.0008219652,0.0006788644,0.0004015016,0.0003262756],"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":[0.0001080014,0.00006125935,0.01360232,0.0001550054,0.0000920024,0.0003200267,0.0005193565,0.6578303,0.00143355,0.2968612,0.004207602,0.02480938],"study_design_scores_gemma":[0.000004784356,0.00001232657,0.001162926,0.00001380946,0.000004185544,0.00004760178,0.00007598738,0.9404001,0.0001608611,0.05680525,0.001304205,0.000007933833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1452326,0.001730458,0.8408518,0.002330198,0.0001204308,0.0001651468,0.001445437,0.001297791,0.00682619],"genre_scores_gemma":[0.9067824,0.00117777,0.08735786,0.0002260843,0.0001383457,0.0001772416,0.001549631,0.000150934,0.002439638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008886844,"threshold_uncertainty_score":0.01767021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4261622023641214,"score_gpt":0.3266026836069348,"score_spread":0.0995595187571866,"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."}}