{"id":"W1987266963","doi":"10.1007/s12008-014-0230-7","title":"A methodology to form families of products by applying fuzzy logic","year":2014,"lang":"en","type":"article","venue":"International Journal on Interactive Design and Manufacturing (IJIDeM)","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Fuzzy logic; Mass customization; Variety (cybernetics); Computer science; Product (mathematics); Process (computing); Product design; Industrial engineering; Personalization; Management science; Systems engineering; Artificial intelligence; Mathematics; Engineering; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001122956,0.0002052463,0.0002609481,0.0006029436,0.0001283717,0.0002300333,0.0003456503,0.00005447294,0.00006369721],"category_scores_gemma":[0.0008933496,0.0001695565,0.00005626899,0.00009481113,0.00003573214,0.0009225422,0.0001473204,0.0002475691,0.00005820961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008090399,"about_ca_system_score_gemma":0.00001847722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003542469,"about_ca_topic_score_gemma":0.000001988915,"domain_scores_codex":[0.9986412,0.00007132879,0.0004105924,0.0003025691,0.000360926,0.0002133757],"domain_scores_gemma":[0.9987531,0.000281331,0.0004570108,0.0001025881,0.0003783351,0.00002757913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006102566,0.0006577373,0.002251794,0.0002599474,0.001297123,0.00006367178,0.002595114,0.006035343,0.1459421,0.05218385,0.06743948,0.7151713],"study_design_scores_gemma":[0.00256561,0.0002965174,0.009210165,0.0005351984,0.0001139513,0.0001596905,0.000884517,0.001833455,0.4820339,0.1009812,0.4004138,0.000971955],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5222094,0.0000775387,0.4517622,0.0101051,0.00434976,0.0009041596,0.000004724753,0.000110483,0.01047661],"genre_scores_gemma":[0.9840015,0.00004327755,0.01034968,0.00409673,0.001061938,0.00003346231,0.00001455091,0.00002385601,0.0003750274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7141993,"threshold_uncertainty_score":0.6914312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04140228255254432,"score_gpt":0.2793114143881512,"score_spread":0.2379091318356069,"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."}}