{"id":"W1991674311","doi":"10.1007/s00477-006-0090-1","title":"Probabilistic risk analysis using ordered weighted averaging (OWA) operators","year":2006,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Probabilistic logic; Parametric statistics; Reliability (semiconductor); Computational intelligence; Uncertainty quantification; Mathematics; Sensitivity (control systems); Computer science; Probabilistic risk assessment; Mathematical optimization; Statistics; Artificial intelligence; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.005769184,0.0003321526,0.0005481252,0.0009229669,0.001261027,0.0004979302,0.0005605749,0.0001295971,0.0003646593],"category_scores_gemma":[0.0009301582,0.0002399113,0.0001633913,0.001653489,0.0007529008,0.0002570301,0.0004530234,0.0007854893,0.00005876718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005628405,"about_ca_system_score_gemma":0.0001568325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009130884,"about_ca_topic_score_gemma":0.0001027184,"domain_scores_codex":[0.9935114,0.000844511,0.0008094079,0.0011212,0.002838358,0.0008751508],"domain_scores_gemma":[0.9961474,0.002348451,0.0002389689,0.0007822811,0.0001101318,0.0003727599],"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":[0.0000813015,0.000739797,0.1645994,0.0000143999,0.0005529137,0.00004495177,0.0002405959,0.8207735,0.001020955,0.00323442,0.0002454419,0.008452338],"study_design_scores_gemma":[0.0006392881,0.0002393573,0.1425941,0.00001808203,0.0003192787,0.000008288645,0.0006559879,0.8085368,0.00003697508,0.04648281,0.000133045,0.0003359893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.540336,0.0003088588,0.458476,0.00002791142,0.00007601258,0.000433444,0.000121811,0.00002741581,0.0001926487],"genre_scores_gemma":[0.9838787,0.0001533845,0.01516776,0.000004815402,0.0001126378,0.00005708384,0.00003484692,0.00002976456,0.0005609999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4435428,"threshold_uncertainty_score":0.97833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05705977011967664,"score_gpt":0.371685771800195,"score_spread":0.3146260016805184,"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."}}