{"id":"W2028517129","doi":"10.1007/bf02984028","title":"Concept optimization for mechanical product using Genetic Algorithm","year":2005,"lang":"en","type":"article","venue":"Journal of Mechanical Science and Technology","topic":"Color perception and design","field":"Psychology","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Crossover; Design structure matrix; Conceptual design; Genetic algorithm; Process (computing); Computer science; Mathematical optimization; Engineering design process; Selection (genetic algorithm); Engineering optimization; Product (mathematics); Algorithm; Function (biology); Optimization problem; Artificial intelligence; Engineering; Mathematics; Systems engineering; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.001063502,0.00009825137,0.0002297448,0.0003989795,0.0001797575,0.00003372453,0.0003517488,0.0001678884,0.0004022244],"category_scores_gemma":[0.0003963973,0.00007895654,0.00004904569,0.000757033,0.0003477749,0.0001710834,0.00006982856,0.0002208955,0.000006725417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000975285,"about_ca_system_score_gemma":0.00016684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002500007,"about_ca_topic_score_gemma":0.000001079397,"domain_scores_codex":[0.9986522,0.00003608182,0.0004199558,0.0002849581,0.0002940751,0.0003127103],"domain_scores_gemma":[0.9988563,0.00005233711,0.000232675,0.0001743597,0.0005547368,0.0001296102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006596644,0.0001990121,0.00001045765,0.000002671036,0.00002068989,0.00001331816,0.0001138104,0.0006817752,0.07234936,0.0257815,0.0006679512,0.9000935],"study_design_scores_gemma":[0.005885406,0.005536559,0.0002750832,0.00007358188,0.000233296,0.00618837,0.002053331,0.8641803,0.05900997,0.0291837,0.02663314,0.0007473051],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06789708,0.0003921632,0.9254863,0.005168179,0.0006771722,0.0002688164,0.000002402386,0.00004274515,0.00006517704],"genre_scores_gemma":[0.5126684,0.00003621519,0.4865789,0.0003273681,0.0003254277,0.00000714751,1.856183e-7,0.000008619164,0.00004778217],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8993462,"threshold_uncertainty_score":0.4404075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03706642480614992,"score_gpt":0.3435489008389314,"score_spread":0.3064824760327815,"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."}}