{"id":"W3170599955","doi":"10.1016/j.engstruct.2021.112634","title":"Identifying and selecting critical connections for seismic response of steel moment resisting frames","year":2021,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fragility; Structural engineering; Moment (physics); Hinge; Beam (structure); Frame (networking); Joint (building); Rotation (mathematics); Displacement (psychology); Seismic loading; Progressive collapse; Column (typography); Ductility (Earth science); Deformation (meteorology); Plastic hinge; Engineering; Earthquake engineering; Connection (principal bundle); Creep; Geology; Mathematics; Geometry; Reinforced concrete; Materials science; Mechanical engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003910743,0.001086476,0.0003904112,0.001452528,0.0005095595,0.0005815033,0.0005466375,0.0007616944,0.002083699],"category_scores_gemma":[0.002016588,0.0003520612,0.000206027,0.0003810052,0.0003855345,0.0005815934,0.0002953446,0.0002480113,0.0003174104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000359635,"about_ca_system_score_gemma":0.000530275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524341,"about_ca_topic_score_gemma":0.003737122,"domain_scores_codex":[0.9998291,0.00005108574,0.000007276716,0.00003345942,0.00004471307,0.00003435175],"domain_scores_gemma":[0.9993492,0.000312935,0.0001301137,0.00003488062,0.0001227721,0.00005009439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00130688,0.0003834568,0.01896584,0.0002655383,0.0000425732,0.0006170542,0.0004541285,0.3832929,0.4520969,0.005340564,0.0009231323,0.136311],"study_design_scores_gemma":[0.00002471331,0.0006116697,0.01062099,0.0000247901,0.00004265677,0.00008887459,0.000347254,0.9139555,0.07195661,0.001710056,0.000593524,0.00002340095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8699708,0.000069022,0.1273855,0.00002728789,0.000007783013,0.00003840297,0.00005423693,0.0002684476,0.00217863],"genre_scores_gemma":[0.9908084,0.00001959154,0.008841406,0.000001647673,0.000001713863,0.00001118434,0.00003299113,0.00001336733,0.000269655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002083699,"threshold_uncertainty_score":0.006970644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093651945101981,"score_gpt":0.2548794412916502,"score_spread":0.2439429218406304,"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."}}