{"id":"W3156863886","doi":"10.3390/sym13040663","title":"Disassembly Sequence Planning for Intelligent Manufacturing Using Social Engineering Optimizer","year":2021,"lang":"en","type":"article","venue":"Symmetry","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"State Key Laboratory of Robotics; National Natural Science Foundation of China","keywords":"Computer science; Reducer; Sequence (biology); Digital signal processing; Mathematical optimization; Graph; Product (mathematics); Engineering; Mathematics; Theoretical computer science","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":[0.0006694134,0.001123343,0.0008723107,0.0009975462,0.0006415989,0.0006899306,0.0006133181,0.000660415,0.001633952],"category_scores_gemma":[0.001045978,0.0003715559,0.0009482132,0.0007747414,0.0005786766,0.0007102414,0.0007808011,0.0005299609,0.000159813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00112425,"about_ca_system_score_gemma":0.001619834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006662203,"about_ca_topic_score_gemma":0.005847634,"domain_scores_codex":[0.9995091,0.0001424175,0.00002955337,0.0001098823,0.0001464587,0.00006266277],"domain_scores_gemma":[0.9996668,0.0001763748,0.00005225622,0.00002058534,0.00006502864,0.00001886853],"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":[0.00003249172,0.00004560157,0.0005577468,0.00005761028,0.0000327159,0.00005042673,0.00005397882,0.9516193,0.001851539,0.009221588,0.000455835,0.03602109],"study_design_scores_gemma":[0.000006426312,0.0000207146,0.00009411757,0.0000025403,0.000007253119,0.000007454809,0.00000919458,0.9969442,0.0003856004,0.002133587,0.0003857912,0.000003033028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02346573,0.0001388101,0.972995,0.0001147741,0.00002061413,0.0000660177,0.00003102786,0.0001802734,0.00298767],"genre_scores_gemma":[0.6237665,0.0002748259,0.3719445,0.00009131833,0.00003062742,0.000389159,0.0001777243,0.00007880777,0.003246685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006662203,"threshold_uncertainty_score":0.01324683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03948066712913388,"score_gpt":0.2781330637508466,"score_spread":0.2386523966217127,"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."}}