{"id":"W2291891274","doi":"10.37686/ser.v1i2.78","title":"Epidemiological geographic profiling for a meta-population network","year":2020,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Profiling (computer programming); Geography; Epidemiology; Population; Outbreak; Data science; Demography; Computer science; Medicine; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02112184,0.001752803,0.002103218,0.008196956,0.001094659,0.004093011,0.003179105,0.001995892,0.01658516],"category_scores_gemma":[0.0763685,0.001283051,0.006118633,0.007169088,0.001068244,0.005339221,0.004053527,0.003185028,0.003072306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001912912,"about_ca_system_score_gemma":0.002598375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007927697,"about_ca_topic_score_gemma":0.007052971,"domain_scores_codex":[0.9918712,0.005128201,0.0005293787,0.001666027,0.0005954519,0.0002096847],"domain_scores_gemma":[0.9493866,0.03704273,0.003489671,0.008003159,0.001304887,0.0007729982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001079081,0.0001867277,0.04870867,0.002254051,0.006068057,0.001119456,0.001039244,0.3162839,0.0007270219,0.3759948,0.06914308,0.1773959],"study_design_scores_gemma":[0.0001752138,0.0001071033,0.003103771,0.0002505915,0.0007557479,0.0003801968,0.0001472898,0.5485745,0.0002098275,0.4254769,0.02077449,0.00004425517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01124923,0.001794654,0.9556965,0.003424942,0.0002797484,0.0003688725,0.02245588,0.002288714,0.00244138],"genre_scores_gemma":[0.2304391,0.003425445,0.7199764,0.0009071176,0.0009941203,0.002407463,0.0348095,0.000610927,0.006429858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02112184,"threshold_uncertainty_score":0.1117043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6186020255060326,"score_gpt":0.485141106366096,"score_spread":0.1334609191399366,"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."}}