{"id":"W2901606588","doi":"10.1177/1045389x18806393","title":"Feasibility of using reduced length superelastic shape memory alloy strands in post-tensioned steel beam–column connections","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Material Systems and Structures","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; CMC Microsystems","keywords":"Materials science; Dissipation; Shape-memory alloy; Structural engineering; Stiffness; Beam (structure); Finite element method; Alloy; Ductility (Earth science); Column (typography); Composite material; Connection (principal bundle); Engineering","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":[0.0001567786,0.0003489404,0.0001389221,0.0002964092,0.0001140245,0.0002111072,0.0006141839,0.0003340406,0.0008837407],"category_scores_gemma":[0.0003196775,0.0001728199,0.0001839592,0.0001573708,0.0001792201,0.000329488,0.0001678711,0.0001395826,0.0003316544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001468253,"about_ca_system_score_gemma":0.0001775202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004553791,"about_ca_topic_score_gemma":0.001942342,"domain_scores_codex":[0.9999056,0.00001583876,0.000006513459,0.00001986011,0.00003952269,0.00001257925],"domain_scores_gemma":[0.999714,0.00003893823,0.0001044953,0.0000523292,0.00006635623,0.00002383742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002004033,0.00008932817,0.003383083,0.0001306566,0.00002456525,0.0002407194,0.00008739438,0.02531827,0.9440311,0.0005374544,0.0001472929,0.02580985],"study_design_scores_gemma":[0.00004596831,0.003978461,0.01621746,0.00003129647,0.0001204575,0.0004214371,0.000119725,0.1578424,0.8150811,0.0003818261,0.005722639,0.00003722847],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778095,0.0002025838,0.02064309,0.00002743777,0.0000231468,0.00001927555,0.00004689308,0.0001680571,0.001060063],"genre_scores_gemma":[0.9907824,0.00006819965,0.008502064,0.00000547633,0.000003067759,0.000008847427,0.00003069804,0.000009255725,0.0005899799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008837407,"threshold_uncertainty_score":0.00295639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02337654045171846,"score_gpt":0.2555930374214443,"score_spread":0.2322164969697258,"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."}}