{"id":"W2115508435","doi":"10.1002/eqe.1151","title":"Development of fragility functions as a damage classification/prediction method for steel moment‐resisting frames using a wavelet‐based damage sensitive feature","year":2011,"lang":"en","type":"article","venue":"Earthquake Engineering & Structural Dynamics","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Samsung","keywords":"Fragility; Wavelet; Structural engineering; Structural health monitoring; Acceleration; Moment (physics); Feature (linguistics); Probabilistic logic; Engineering; Computer science; Artificial intelligence","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":[0.0003142593,0.0004410523,0.0004332469,0.0003040231,0.0002772984,0.00003606317,0.0002078164,0.0003034456,0.00001493308],"category_scores_gemma":[0.0001379903,0.0004699967,0.0001381765,0.0004199611,0.00004741506,0.0002352969,0.00004979622,0.0005199827,0.000001209629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005804848,"about_ca_system_score_gemma":0.000103564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006257049,"about_ca_topic_score_gemma":0.00003706245,"domain_scores_codex":[0.9980716,0.00003825327,0.0006436154,0.0004187702,0.0003017886,0.0005260399],"domain_scores_gemma":[0.9987885,0.0001898157,0.0001732857,0.0004456062,0.0002472021,0.0001555367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002096376,0.00003780693,0.00256001,0.002498398,0.0004892108,0.00001403111,0.005378571,0.07324151,0.02761193,0.003725199,0.00005901289,0.8841747],"study_design_scores_gemma":[0.0001813218,0.00003117215,0.4754963,0.0000809607,0.00003450266,0.000007260313,0.0002036951,0.5205378,0.00303827,0.00006985977,0.00008501178,0.0002339152],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6250131,0.00001498908,0.3729123,0.000009273765,0.0006636388,0.0004641236,0.0002031997,0.000663955,0.00005541496],"genre_scores_gemma":[0.5573357,0.000001085714,0.4423301,0.000004753844,0.00008259367,0.00004032433,0.000130398,0.00005745841,0.00001753335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8839408,"threshold_uncertainty_score":0.9997752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0337728711175614,"score_gpt":0.2824889455341413,"score_spread":0.2487160744165799,"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."}}