{"id":"W1731569230","doi":"10.1371/journal.pone.0130948","title":"Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Institut Périmètre de physique théorique; Industry Canada; Government of Canada; Santa Fe Institute","keywords":"Systemic risk; Financial networks; Leverage (statistics); Degree distribution; Financial contagion; Network topology; Complex network; Creditor; Computer science; Economics; Shock (circulatory); Network theory; Financial crisis; Econometrics; Mathematics; Finance; Financial market; Statistics; Debt; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005352061,0.0001043418,0.0004460365,0.0001126304,0.00007444365,0.00007463676,0.00008780812,0.00009337402,0.00005021889],"category_scores_gemma":[0.000659961,0.0001217811,0.00004765734,0.0002828355,0.00004346747,0.0001361157,0.00006551165,0.000151373,0.00006947807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001058988,"about_ca_system_score_gemma":0.00001282465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001748714,"about_ca_topic_score_gemma":0.0003876781,"domain_scores_codex":[0.9989209,0.00002896815,0.0004850584,0.0002780472,0.00004609899,0.000240897],"domain_scores_gemma":[0.9994583,0.00006088769,0.0001442399,0.0001847619,0.0000482651,0.0001035305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003893835,0.0004060793,0.8454841,0.0002131166,0.00015195,0.00001510102,0.0005837309,0.0003708069,0.00001287221,0.1510022,0.0008038317,0.0009172941],"study_design_scores_gemma":[0.002407796,0.000283189,0.5420308,0.0008679248,0.0001134563,0.00002264585,0.001134233,0.4034659,0.00002137128,0.04029501,0.008220806,0.001136908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704303,0.008255255,0.01895145,0.0004308771,0.00009865898,0.0001849212,0.0001821894,0.00003321033,0.001433177],"genre_scores_gemma":[0.9991063,0.0001475887,0.0003499793,0.0000173344,0.0001914361,0.00002270017,0.00003002493,0.000009601961,0.0001250681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4030951,"threshold_uncertainty_score":0.4966089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09354968688122478,"score_gpt":0.2092102424143731,"score_spread":0.1156605555331483,"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."}}