{"id":"W2119841678","doi":"10.1007/s12289-008-0051-y","title":"Experimental validation of numerical sensitivities in a deep drawing simulation","year":2008,"lang":"en","type":"article","venue":"International Journal of Material Forming","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Larus Technologies (Canada)","funders":"","keywords":"Blank; Benchmark (surveying); Pillar; Nonlinear system; Sensitivity (control systems); Deep drawing; Software; Simple (philosophy); Materials science; Computational intelligence; Structural engineering; Computer science; Engineering; Artificial intelligence; Composite material; Physics; Electronic engineering; Geology","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":[0.001291359,0.0007092853,0.000428056,0.000536248,0.0004170412,0.0006609222,0.000958564,0.001245242,0.003951839],"category_scores_gemma":[0.007400092,0.0004005681,0.0002624734,0.0005057012,0.0009330048,0.0006881711,0.001060248,0.0007609798,0.0004981924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003844516,"about_ca_system_score_gemma":0.0003947605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001055487,"about_ca_topic_score_gemma":0.000682929,"domain_scores_codex":[0.9987244,0.0002871958,0.00006570702,0.0001607876,0.0006312727,0.0001306753],"domain_scores_gemma":[0.9945922,0.003022292,0.0002875144,0.0009760659,0.0009714073,0.0001505021],"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":[0.0020942,0.0007359811,0.00474271,0.0003806881,0.00004511062,0.0004236983,0.0005722205,0.3760951,0.5789564,0.004202796,0.001331695,0.03041943],"study_design_scores_gemma":[0.00009521226,0.001000331,0.00451093,0.00003279408,0.00002440291,0.0001550019,0.00009832432,0.5758737,0.4162115,0.0006075993,0.00133855,0.00005172564],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9308249,0.0001779935,0.05800561,0.0002128303,0.0001226525,0.000105393,0.0004991508,0.001173403,0.008878067],"genre_scores_gemma":[0.9935926,0.00002908641,0.005650606,0.00002353684,0.000004112286,0.00001526009,0.0000897095,0.00005146324,0.0005435534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003951839,"threshold_uncertainty_score":0.01322025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01821532748078933,"score_gpt":0.2837848625862941,"score_spread":0.2655695351055047,"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."}}