{"id":"W1874907880","doi":"10.1016/j.cirpj.2015.09.004","title":"Product family formation by matching Bill-of-Materials trees","year":2015,"lang":"en","type":"article","venue":"CIRP journal of manufacturing science and technology","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Similarity (geometry); Product (mathematics); Matching (statistics); Tree (set theory); Component (thermodynamics); Field (mathematics); Computer science; Process (computing); Cluster analysis; Hierarchical clustering; Variety (cybernetics); Tree structure; Industrial engineering; Engineering; Mathematics; Artificial intelligence; Algorithm; Binary tree; Combinatorics","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":[0.001474949,0.00008607267,0.0001761289,0.00084423,0.0001292303,0.000141061,0.0003386512,0.00004072699,0.000003473609],"category_scores_gemma":[0.0002957486,0.00005948133,0.0000115542,0.0004767829,0.0002216323,0.002370307,0.0001253801,0.00008242002,0.000005503714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003469601,"about_ca_system_score_gemma":0.00007351956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002017145,"about_ca_topic_score_gemma":0.000001342715,"domain_scores_codex":[0.999006,0.00000404241,0.0003380405,0.0001323655,0.0003503101,0.0001691817],"domain_scores_gemma":[0.9988985,0.000006023612,0.0005366534,0.0001105586,0.0004348171,0.00001341682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007278591,0.00008020343,0.002623347,0.000142677,0.00002379758,0.00001020442,0.0004367745,0.0001120452,0.8794273,0.00289239,0.00763786,0.1065406],"study_design_scores_gemma":[0.0003973258,0.0000251882,0.002268684,0.00006350577,0.00001741784,0.00003943247,0.0007458944,0.00002042966,0.9681869,0.02101688,0.00710106,0.0001173492],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963432,0.0002328192,0.0004023081,0.002148814,0.000413692,0.00007875582,3.619698e-7,0.00003272752,0.0003473128],"genre_scores_gemma":[0.999263,0.00003068982,0.0004434327,0.00009204302,0.0001464387,0.000001164321,0.000001389219,0.00000518815,0.00001662315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1064233,"threshold_uncertainty_score":0.2425578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0155105967496475,"score_gpt":0.2144473819297648,"score_spread":0.1989367851801173,"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."}}