{"id":"W3125499280","doi":"10.1002/eqe.3418","title":"Clustering‐based adaptive ground motion selection algorithm for efficient estimation of structural fragilities","year":2021,"lang":"en","type":"article","venue":"Earthquake Engineering & Structural Dynamics","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Fragility; Cluster analysis; Algorithm; Seismic hazard; Set (abstract data type); Computer science; Ground motion; Consistency (knowledge bases); Incremental Dynamic Analysis; Structural engineering; Artificial intelligence; Engineering; Geology; Seismology","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":[0.00006884265,0.0002484859,0.0002911099,0.0001656302,0.00008844155,0.00004472494,0.00008595922,0.0001082998,0.00002153446],"category_scores_gemma":[0.00002784183,0.0002661057,0.0001600808,0.0003735053,0.00002533967,0.0001749707,0.00001718281,0.0001714578,0.000001273044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000202486,"about_ca_system_score_gemma":0.00002701068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002719099,"about_ca_topic_score_gemma":0.00004979529,"domain_scores_codex":[0.9989181,0.0000113337,0.000347195,0.0002179471,0.0002040658,0.0003014225],"domain_scores_gemma":[0.9995207,0.000060884,0.00006072317,0.0001603996,0.0001441955,0.00005306037],"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":[0.000004284239,0.000001921478,0.00005114613,0.0001088695,0.00005273357,7.901959e-7,0.00008349326,0.7318602,0.0001577386,0.000117093,0.000001476706,0.2675603],"study_design_scores_gemma":[0.0003397391,0.00003938242,0.08927853,0.00004683227,0.0000518268,0.00001568451,0.00009133959,0.9085914,0.001229885,0.0000629375,0.000007332786,0.0002451439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4986248,0.00007020577,0.5006487,0.000005109885,0.000348997,0.00008565452,0.00007214496,0.0001374464,0.000006909679],"genre_scores_gemma":[0.9393893,0.0000038097,0.0599777,0.000006288435,0.0001072262,0.00001726408,0.000429295,0.0000402081,0.00002890244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4407645,"threshold_uncertainty_score":0.9999791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005482578891123592,"score_gpt":0.2015418872344137,"score_spread":0.1960593083432901,"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."}}