{"id":"W2153022398","doi":"10.1017/s0890060404040077","title":"Evaluation and selection in product design for mass customization: A knowledge decision support approach","year":2004,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; National Institute of Standards and Technology","keywords":"Mass customization; Product design; Ranking (information retrieval); Personalization; Selection (genetic algorithm); Product engineering; Computer science; Fuzzy logic; Product (mathematics); New product development; Product design specification; Systems engineering; Engineering; Knowledge management; Process management; Artificial intelligence; Marketing; Business; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002363801,0.0001940521,0.0002500094,0.0009082967,0.0001740729,0.0002628723,0.00008253571,0.0000649272,0.000009826887],"category_scores_gemma":[0.0003416688,0.0001932546,0.00006322096,0.000729326,0.00001614942,0.0006573625,0.00002415337,0.00006268642,0.000003392823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001045914,"about_ca_system_score_gemma":0.00004344665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002790973,"about_ca_topic_score_gemma":0.00003814088,"domain_scores_codex":[0.9986771,0.00001309802,0.0004020515,0.0004719566,0.0001827768,0.0002530112],"domain_scores_gemma":[0.9994872,0.00009162035,0.0001130116,0.00007654828,0.0002133519,0.00001825508],"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":[0.0000970361,0.0000474883,0.00006267876,0.00008801655,0.00009841966,1.632174e-7,0.0001644274,0.9034359,0.002010449,0.001726897,0.00002073638,0.09224772],"study_design_scores_gemma":[0.000205894,0.00002095717,0.0005299289,0.00002347478,0.0005251191,7.578749e-7,0.00007223042,0.933499,0.05329766,0.01130577,0.0002642187,0.0002549645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02034572,0.0001038507,0.9779747,0.00005960621,0.00009715751,0.001326039,5.906527e-7,0.00006841896,0.00002391857],"genre_scores_gemma":[0.863267,0.0000205836,0.1360796,0.00001633599,0.0002473094,0.0002974432,0.00003628159,0.00002234551,0.00001312344],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8429213,"threshold_uncertainty_score":0.7880695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04859112681829944,"score_gpt":0.2688663267317234,"score_spread":0.220275199913424,"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."}}