{"id":"W3203172674","doi":"","title":"Universal nonlinear infection kernel from heterogeneous exposure on higher-order networks","year":2021,"lang":"en","type":"article","venue":"Queen Mary Research Online (Queen Mary University of London)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Canada First Research Excellence Fund; China Scholarship Council; Royal Society","keywords":"Nonlinear system; Leverage (statistics); Statistical physics; Computer science; Corollary; Kernel (algebra); Econometrics; Biological system; Biology; Theoretical computer science; Mathematics; Physics; Artificial intelligence; Pure mathematics; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003872238,0.0003504688,0.0006028123,0.0003771914,0.0004157407,0.00006112159,0.000633067,0.0002100533,0.004713793],"category_scores_gemma":[0.00001667642,0.000423358,0.0004000113,0.001271917,0.0003207763,0.0003225768,0.0008679354,0.001206518,0.00005504256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002592475,"about_ca_system_score_gemma":0.0003939259,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02073761,"about_ca_topic_score_gemma":0.0008400952,"domain_scores_codex":[0.9964088,0.0009760124,0.0003516241,0.0008140285,0.0007754614,0.0006741364],"domain_scores_gemma":[0.9971589,0.0005371178,0.0001975864,0.0009331279,0.0008887885,0.000284469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003032081,0.006553266,0.738833,0.0001320719,0.004051535,0.002792418,0.0003627238,0.04328223,0.001176293,0.006789907,0.07935328,0.1136412],"study_design_scores_gemma":[0.007571555,0.002434198,0.3186538,0.0007794959,0.0007776042,0.000009192089,0.001412395,0.05956216,0.002491483,0.009630449,0.5941209,0.002556783],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706109,0.0002358302,0.01240774,0.002652336,0.0002022993,0.0005830019,0.0007270867,0.0002640679,0.01231676],"genre_scores_gemma":[0.9669531,0.0007429376,0.009727539,0.0001067565,0.001186591,0.0000018113,0.004586613,0.00007350412,0.01662113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5147676,"threshold_uncertainty_score":0.9998218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02256233929851571,"score_gpt":0.2804749195543008,"score_spread":0.2579125802557851,"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."}}