{"id":"W4405406760","doi":"10.1007/s00366-024-02091-y","title":"Integrating analytical and machine learning methods for investigating nonlinear bending and post-buckling behavior of 3D-printed auxetic tubes","year":2024,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Cellular and Composite Structures","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Materials science; Buckling; Nonlinear system; Auxetics; Structural engineering; Finite element method; Digital image correlation; Bending; Ultimate tensile strength; Gradient boosting; Computer science; Artificial intelligence; Algorithm; Composite material; Engineering; Random forest; 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":[0.0005724863,0.0004518582,0.0003181985,0.0006473183,0.0003041824,0.0005519484,0.0005242385,0.0007367174,0.0007233843],"category_scores_gemma":[0.001464989,0.0002619412,0.0003186226,0.0004072752,0.00055727,0.0007241009,0.0003868931,0.0004532209,0.0001824038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004953655,"about_ca_system_score_gemma":0.0005213713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001369037,"about_ca_topic_score_gemma":0.00207666,"domain_scores_codex":[0.9998211,0.00004732486,0.00001030023,0.00002695044,0.00007890407,0.00001541744],"domain_scores_gemma":[0.9992504,0.0004369541,0.000108536,0.00006059013,0.0001227361,0.00002084638],"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":[0.00005827535,0.0001494834,0.002037525,0.0001379204,0.00003551087,0.00008136003,0.0001016645,0.8732897,0.04917768,0.007984376,0.0002264527,0.06672006],"study_design_scores_gemma":[5.790932e-7,0.000005032653,0.00009950215,0.000001353562,0.000001068222,0.000005956389,0.000003262556,0.9971705,0.00218858,0.0004651635,0.00005659759,0.000002325623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1827203,0.0003578459,0.8121377,0.0001607997,0.00003611339,0.0000437995,0.00004888984,0.0006878214,0.003806755],"genre_scores_gemma":[0.8714701,0.0002784845,0.1254676,0.00005094838,0.00002030863,0.00006803734,0.0000438537,0.00005713495,0.002543665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001369037,"threshold_uncertainty_score":0.0035941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008108998496540499,"score_gpt":0.2531261547756341,"score_spread":0.2450171562790936,"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."}}