{"id":"W2366175431","doi":"","title":"The constructing principle of event tree method and its application in risk analysis for dykes and dams","year":2006,"lang":"en","type":"article","venue":"Journal of China Institute of Water Resources and Hydropower Research","topic":"Geoscience and Mining Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Event tree analysis; Event tree; Event (particle physics); Fault tree analysis; Reliability (semiconductor); Tree (set theory); Risk analysis (engineering); Flexibility (engineering); Stability (learning theory); Computer science; Field (mathematics); Mathematics; Engineering; Statistics; Reliability engineering; Business; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002014425,0.00006466373,0.0002302276,0.0004214548,0.0001128771,0.00002775266,0.0001451371,0.00005117598,6.925492e-7],"category_scores_gemma":[0.00006575054,0.00003775844,0.00004068957,0.0002519852,0.0002645801,0.0001057693,0.00007592449,0.0002068414,4.240715e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001277948,"about_ca_system_score_gemma":0.000008501644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002112447,"about_ca_topic_score_gemma":0.0003158758,"domain_scores_codex":[0.9991207,0.00004141103,0.0003652265,0.0000972798,0.0001934568,0.000181977],"domain_scores_gemma":[0.9995958,0.00008143815,0.0001104619,0.00009547915,0.00008465888,0.00003221642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005326988,0.0002256728,0.4555553,0.0009299979,0.0009600942,0.00002582539,0.01148762,0.09480043,0.1990484,0.009395191,0.00003887289,0.2269999],"study_design_scores_gemma":[0.00434753,0.001671086,0.1759392,0.0003931119,0.0005433784,0.0002933695,0.002791776,0.4003106,0.2820897,0.03650123,0.0945839,0.000535056],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897636,0.001432066,0.008390835,0.0001506547,0.00002226098,0.0001323216,0.000003260614,0.000003883074,0.0001011029],"genre_scores_gemma":[0.9969733,0.0004983542,0.002476853,5.253525e-7,0.0000203648,0.000004240778,6.363882e-7,0.000004720278,0.00002100953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3055102,"threshold_uncertainty_score":0.1539745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01674264295750687,"score_gpt":0.3280274096254833,"score_spread":0.3112847666679764,"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."}}