{"id":"W2947079740","doi":"10.1177/1369433219852042","title":"Nonlinear buckling optimization technique to predict critical imperfection wavelength of combined liquid-filled steel conical tanks","year":2019,"lang":"en","type":"article","venue":"Advances in Structural Engineering","topic":"Composite Structure Analysis and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Buckling; Conical surface; Finite element method; Wavelength; Nonlinear system; Structural engineering; Critical load; Eigenvalues and eigenvectors; Materials science; Mathematics; Engineering; Geometry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000304198,0.0004296297,0.0003220171,0.0004511616,0.0002037482,0.000222557,0.0003514862,0.0004637564,0.001025974],"category_scores_gemma":[0.0006695272,0.0002187373,0.0003505666,0.0002246041,0.0002267046,0.0002600309,0.0002698892,0.0003078528,0.0002065217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000330733,"about_ca_system_score_gemma":0.000815673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003445775,"about_ca_topic_score_gemma":0.00497175,"domain_scores_codex":[0.9999136,0.00001898899,0.000005227377,0.00001514879,0.00003684828,0.00001024846],"domain_scores_gemma":[0.9997016,0.000157032,0.00005034942,0.0000237715,0.00005779849,0.000009481267],"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":[0.0000432106,0.00003990435,0.001703441,0.00006836534,0.00001488361,0.00004297093,0.00005316023,0.9616589,0.02087475,0.001093008,0.0001325494,0.01427483],"study_design_scores_gemma":[0.000001331588,0.00001203076,0.0002696735,0.000001974618,0.000001705113,0.000005669348,0.000004809544,0.9972327,0.0022871,0.000108497,0.00007224215,0.000002184217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3757843,0.0001447652,0.6167014,0.00007583732,0.00001125731,0.0000648674,0.0001018936,0.0005646579,0.006551001],"genre_scores_gemma":[0.9098915,0.00005694074,0.08815417,0.00001483202,0.000002499996,0.00007241139,0.00007407501,0.00006067545,0.001672863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003445775,"threshold_uncertainty_score":0.006851435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002050955503242257,"score_gpt":0.223378247873225,"score_spread":0.2213272923699827,"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."}}