{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009020641,0.0001957383,0.0002024914,0.00009628406,0.0001307212,0.0001021582,0.0001098986,0.0001111856,0.00002963298],"category_scores_gemma":[0.00003369701,0.0002216067,0.00009233018,0.0001213792,0.000009695032,0.0001719484,0.00004340715,0.0001465576,0.000002996208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001294256,"about_ca_system_score_gemma":0.00002204587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000032486,"about_ca_topic_score_gemma":4.608618e-7,"domain_scores_codex":[0.9990604,0.000007247253,0.0002276396,0.000226723,0.0001293094,0.0003487173],"domain_scores_gemma":[0.9996805,0.00005781209,0.00003306006,0.0001248169,0.00004001105,0.00006382845],"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":[0.000004748293,0.000008058859,0.00004169597,0.0003941498,0.00005129917,0.00001305714,0.0003085728,0.9913178,0.003734029,0.0002977251,0.00007933963,0.003749494],"study_design_scores_gemma":[0.0002006552,0.000006099617,0.0001291558,0.00009558999,0.00003204605,0.00001595522,0.0001767377,0.5334423,0.4633383,0.0001103185,0.002119504,0.0003332842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2955282,0.0004574336,0.702281,0.00002407744,0.0006372635,0.0001231432,0.00001488364,0.0003346126,0.0005994206],"genre_scores_gemma":[0.9492658,0.00002437394,0.05015202,0.00003547917,0.0003127425,0.00002062086,0.00004029226,0.00007154849,0.0000770664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6537377,"threshold_uncertainty_score":0.9036859,"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."}}