{"id":"W426244849","doi":"10.1007/s00170-015-7304-y","title":"Assembly system synthesis using association rule discovery","year":2015,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Automotive industry; Scope (computer science); Process (computing); Assembly modelling; Product (mathematics); Task (project management); Computer science; Association rule learning; Systems engineering; Design for assembly; Manufacturing engineering; Realization (probability); Engineering; Engineering drawing; Industrial engineering; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001088358,0.001248312,0.001365709,0.002748907,0.0009945442,0.001330223,0.001437905,0.0009210136,0.008175211],"category_scores_gemma":[0.002962774,0.0009129677,0.002446928,0.00174775,0.0005185493,0.001100444,0.0009331289,0.001120014,0.002349061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005918968,"about_ca_system_score_gemma":0.002248842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00675072,"about_ca_topic_score_gemma":0.007325732,"domain_scores_codex":[0.9988499,0.000213053,0.0001251808,0.0003239917,0.000358124,0.0001297245],"domain_scores_gemma":[0.9977868,0.001378562,0.0001458357,0.0002751566,0.0003742483,0.00003951427],"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":[0.0003218126,0.0002549687,0.001599741,0.0005114221,0.0003289787,0.0005119832,0.00009187271,0.2867339,0.01872354,0.007903128,0.003712276,0.6793064],"study_design_scores_gemma":[0.00004504284,0.0001489353,0.0004699171,0.00002692094,0.0001431972,0.0001219155,0.00002969514,0.9724191,0.01485767,0.007936615,0.003779148,0.00002172941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01546025,0.0003515203,0.9732032,0.0001019234,0.0001080987,0.0002257626,0.0004039562,0.006202581,0.003942687],"genre_scores_gemma":[0.2717432,0.000299468,0.7209201,0.0001336056,0.00004933842,0.0002998915,0.001617344,0.0003380449,0.004598965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008175211,"threshold_uncertainty_score":0.02734888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087455567669953,"score_gpt":0.2296188573811153,"score_spread":0.2187443017044157,"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."}}