{"id":"W4390323407","doi":"10.1073/pnas.2312202121","title":"Nonlinear bias toward complex contagion in uncertain transmission settings","year":2023,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Institute of General Medical Sciences; National Institutes of Health; Sentinelle Nord, Université Laval; Fonds de recherche du Québec; Fonds de recherche du Québec – Nature et technologies; Université Laval; Council of State and Territorial Epidemiologists","keywords":"Inference; Computer science; Transmission (telecommunications); Econometrics; Nonlinear system; Emotional contagion; Simple (philosophy); Contingency; Bayesian probability; Bayesian inference; Task (project management); Data science; Artificial intelligence; Psychology; Mathematics; Economics; Telecommunications; Social psychology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007906906,0.0005784734,0.0009932185,0.00109317,0.000808399,0.002340905,0.001423288,0.00159984,0.002811717],"category_scores_gemma":[0.06223217,0.0007470353,0.000773211,0.0006586347,0.003432712,0.00456524,0.002503575,0.003529415,0.0003001859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00176065,"about_ca_system_score_gemma":0.000785831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006434249,"about_ca_topic_score_gemma":0.005410473,"domain_scores_codex":[0.9970759,0.001750753,0.0001022275,0.0005159968,0.0003697944,0.0001854038],"domain_scores_gemma":[0.9284176,0.05953734,0.006148925,0.003638003,0.001477026,0.0007811105],"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":[0.0001739867,0.00007809948,0.02634267,0.0003537907,0.0001842513,0.001053983,0.001111506,0.4469335,0.003771443,0.4893671,0.002948799,0.02768096],"study_design_scores_gemma":[0.00001900802,0.00002482401,0.003248989,0.0000478925,0.00002180377,0.0002204404,0.00008345418,0.7060208,0.000610312,0.288698,0.0009615104,0.00004279758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2775273,0.0009674192,0.7022809,0.005760558,0.0001111342,0.0001113589,0.0003366851,0.0004052528,0.01249943],"genre_scores_gemma":[0.9648676,0.0006632376,0.0315061,0.0005944191,0.0001555744,0.00006402063,0.0001365775,0.00006819186,0.001944208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007906906,"threshold_uncertainty_score":0.04181617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.501273752856848,"score_gpt":0.4720178712760294,"score_spread":0.02925588158081865,"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."}}