{"id":"W2588675121","doi":"10.1109/ssci.2016.7850071","title":"Estimating force mix lower bounds using a multi-objective evolutionary algorithm","year":2016,"lang":"en","type":"article","venue":"","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Flexibility (engineering); Mathematical optimization; Set (abstract data type); Computer science; Evolutionary algorithm; Matching (statistics); Upper and lower bounds; Point (geometry); Event (particle physics); Multi-objective optimization; Algorithm; Mathematics; Statistics","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":[0.0002284686,0.000174113,0.0001384543,0.000200948,0.000324198,0.0001417218,0.0001418226,0.00006096993,0.0004310893],"category_scores_gemma":[0.0001747098,0.0001233821,0.00005367884,0.0003944274,0.00005119622,0.002093826,0.0001537759,0.00005013162,0.0003635242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001234503,"about_ca_system_score_gemma":0.00005025672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001168361,"about_ca_topic_score_gemma":0.00001154828,"domain_scores_codex":[0.9988973,0.00000683346,0.0002366529,0.0003367993,0.000217921,0.0003045528],"domain_scores_gemma":[0.9994045,0.00002424113,0.0001501151,0.0001607865,0.000248777,0.00001151808],"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":[0.0002985159,0.001135958,0.1293781,0.0003886778,0.0004741156,0.00008525606,0.0005647723,0.0017953,0.0393684,0.0262984,0.03346966,0.7667428],"study_design_scores_gemma":[0.002431886,0.00001156052,0.02021401,0.0002503748,0.00008289507,0.00001293528,0.0001825838,0.954965,0.0006487386,0.008446982,0.01185093,0.000902112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07608809,0.00005551449,0.9124269,0.0005992025,0.001520979,0.0003248193,0.000002032819,0.0003816262,0.008600851],"genre_scores_gemma":[0.6659651,0.00000195378,0.321041,0.0009196436,0.002290758,0.00002571272,0.00002431933,0.00005349707,0.009678028],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9531697,"threshold_uncertainty_score":0.5031375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01674686931760918,"score_gpt":0.2327938837070679,"score_spread":0.2160470143894587,"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."}}