{"id":"W4391983496","doi":"10.1016/j.tust.2024.105659","title":"Optimal earthquake intensity measure in probabilistic seismic demand models of underground subway station structure","year":2024,"lang":"en","type":"article","venue":"Tunnelling and Underground Space Technology","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Foundation Research Project of Jiangsu Province; National Natural Science Foundation of China","keywords":"Measure (data warehouse); Intensity (physics); Probabilistic logic; Seismology; Engineering; Environmental Seismic Intensity scale; Traffic intensity; Structural engineering; Mathematics; Civil engineering; Geology; Forensic engineering; Geotechnical engineering; Computer science; Statistics; Earthquake scenario; Seismic hazard; Data mining; Telecommunications; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002346099,0.000390882,0.0005331049,0.0007116965,0.00007771193,0.0001002195,0.0001986167,0.0005956813,0.000007072222],"category_scores_gemma":[0.00005460576,0.0003783194,0.00007312278,0.0008615681,0.0002461875,0.0002465819,0.00005601559,0.0009260479,0.000001927669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001555716,"about_ca_system_score_gemma":0.0000554665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001264424,"about_ca_topic_score_gemma":0.0003820479,"domain_scores_codex":[0.9982791,0.0000294944,0.0004658627,0.0004943254,0.0002237841,0.0005074569],"domain_scores_gemma":[0.9992809,0.0001668761,0.00004528285,0.0003409043,0.0000766227,0.00008939292],"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":[0.00002104446,0.00001526644,0.00006092644,0.0005077079,0.00008013105,0.00002795022,0.0002831859,0.9185995,0.003655343,0.07451267,0.00004221769,0.002194056],"study_design_scores_gemma":[0.0002973812,0.00009438445,0.0002619394,0.0002741404,0.00004722501,0.0001353009,0.0006023858,0.744846,0.0007738278,0.2521506,0.0001696907,0.0003471242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6634238,0.004268525,0.3303335,0.0004194833,0.0002752397,0.0001901228,0.00001495156,0.0009014902,0.0001728091],"genre_scores_gemma":[0.9964543,0.0005248762,0.002796275,0.00001080849,0.00004347353,0.00001248554,0.00001717767,0.00007041418,0.00007017321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3330305,"threshold_uncertainty_score":0.9998669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009106585884301115,"score_gpt":0.2015381409209888,"score_spread":0.1924315550366877,"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."}}