{"id":"W4392042777","doi":"10.32920/25262794.v1","title":"Simulation and Response Prediction of Shape Memory Alloy (SMA)-based Steel Beam-column Connections with Steel Angles","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"SMA*; Shape-memory alloy; Structural engineering; Materials science; Finite element method; Beam (structure); Pseudoelasticity; Residual; Fracture (geology); Column (typography); Bar (unit); Connection (principal bundle); Composite material; Computer science; Engineering; Martensite; 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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001277541,0.0004017985,0.0004785779,0.0004996077,0.0002224542,0.0002338933,0.0002661735,0.0003635986,0.000918814],"category_scores_gemma":[0.0002352954,0.0003566997,0.0001232456,0.0003200159,0.0003158922,0.0003025334,0.0002251827,0.0004524249,0.00005922016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001732039,"about_ca_system_score_gemma":0.0006641095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001122105,"about_ca_topic_score_gemma":0.000260204,"domain_scores_codex":[0.997282,0.0003165049,0.0007687426,0.0007441497,0.0005922748,0.0002963367],"domain_scores_gemma":[0.9974709,0.001090273,0.0002814748,0.0006379277,0.0003807781,0.0001386604],"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":[0.001147585,0.0001666892,0.0002023508,0.0008869902,0.00007846077,0.000008535552,0.001439975,0.7636989,0.2317377,0.0002466821,0.0001587543,0.0002273643],"study_design_scores_gemma":[0.001463996,0.0006601465,0.01238264,0.0009202419,0.0005456833,0.00001864876,0.001437273,0.8920472,0.08906226,0.0004727179,0.0003951815,0.000594017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895902,0.000155852,0.00453785,0.0004645322,0.0007656161,0.001489018,0.001253427,0.0006181122,0.001125453],"genre_scores_gemma":[0.9945793,0.000007815678,0.004081473,0.00009536315,0.0000949953,0.0002426244,0.0001481691,0.00006373481,0.0006865766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1426754,"threshold_uncertainty_score":0.9999945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03100995821466603,"score_gpt":0.2717094926932479,"score_spread":0.2406995344785819,"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."}}