{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005445896,0.0006888125,0.001726562,0.001626359,0.001032407,0.003536436,0.002403432,0.002443687,0.01452545],"category_scores_gemma":[0.02212954,0.0005962626,0.001527793,0.001944435,0.002579478,0.003634179,0.002770043,0.00388659,0.001336875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001825802,"about_ca_system_score_gemma":0.000700107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005608632,"about_ca_topic_score_gemma":0.003587974,"domain_scores_codex":[0.9973552,0.001057583,0.0001188226,0.0006198157,0.0004511974,0.0003974689],"domain_scores_gemma":[0.9856103,0.008441717,0.002710944,0.00140175,0.001259241,0.0005761074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000150861,0.00008225587,0.006832952,0.00006447298,0.0000680764,0.0006459439,0.0003917627,0.4975497,0.0005942501,0.4785625,0.00406001,0.01099721],"study_design_scores_gemma":[0.00001587239,0.00004654555,0.001348516,0.00002004652,0.00001846776,0.00007919779,0.0001193341,0.8207099,0.0001814435,0.1760921,0.001345257,0.00002326466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2813867,0.0007315008,0.6867573,0.003948445,0.0001708243,0.0001780353,0.00115091,0.0004724852,0.02520371],"genre_scores_gemma":[0.972406,0.0002386582,0.01204326,0.0001394193,0.00007871012,0.0000977048,0.0002898702,0.00004983444,0.0146566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01452545,"threshold_uncertainty_score":0.04859245,"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."}}