{"id":"W2588603926","doi":"10.1017/asb.2018.18","title":"COMMON SHOCK MODELS FOR CLAIM ARRAYS","year":2018,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Dependency (UML); Diagonal; Computer science; Construct (python library); Dimension (graph theory); Matrix (chemical analysis); Diversification (marketing strategy); Set (abstract data type); Interpretation (philosophy); Matching (statistics); Data mining; Theoretical computer science; Mathematics; Artificial intelligence; Statistics; Pure mathematics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003210688,0.0001725584,0.0003349858,0.0001060381,0.0004061799,0.0001780496,0.0009910503,0.0001395038,0.001634254],"category_scores_gemma":[0.001495815,0.0001247226,0.0001765856,0.0002687918,0.0003354654,0.0001180157,0.0002006731,0.0001464224,0.002681987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002969287,"about_ca_system_score_gemma":0.00005681783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005973884,"about_ca_topic_score_gemma":0.00009582773,"domain_scores_codex":[0.9973624,0.0001726672,0.0006387058,0.0006475541,0.0007508823,0.0004277954],"domain_scores_gemma":[0.9966103,0.00173224,0.0001681147,0.0008529241,0.0004835584,0.0001528707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004294053,0.0002307486,0.001607709,0.00001511153,0.00002537373,0.000003300402,0.00149196,0.008592447,0.0003889486,0.09930485,0.7827633,0.1051468],"study_design_scores_gemma":[0.0003742193,0.0002017088,0.0002083431,0.00001450658,0.000007776535,0.000004656413,0.00008818771,0.06213548,0.0007388027,0.5152475,0.4208013,0.0001775348],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09343655,0.0001267971,0.8419131,0.01713452,0.0008592087,0.0007132983,0.0000716899,0.0001525116,0.0455923],"genre_scores_gemma":[0.9584577,0.000005326255,0.03154276,0.001337749,0.0004755637,0.00005596525,0.000005500777,0.00001941784,0.008099983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8650212,"threshold_uncertainty_score":0.9992784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.149085443792414,"score_gpt":0.3727208557443534,"score_spread":0.2236354119519395,"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."}}