{"id":"W4403621975","doi":"","title":"MASS CUSTOMIZATION NEARSHORING PROGRAM FOR CLOTHING MANUFACTURERS","year":2019,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Clothing; Mass customization; Personalization; Business; Manufacturing engineering; Computer science; Engineering; Marketing; Geography; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001341571,0.0004456645,0.0001688491,0.001240744,0.002306572,0.001141462,0.0008943102,0.0008198118,0.05025759],"category_scores_gemma":[0.00233673,0.000348815,0.0003180775,0.0005838255,0.0004774211,0.001542657,0.002515758,0.001045222,0.009616779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000771387,"about_ca_system_score_gemma":0.002071859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001103821,"about_ca_topic_score_gemma":0.00226202,"domain_scores_codex":[0.9991357,0.0002196092,0.00003255243,0.0001583286,0.0002552779,0.000198673],"domain_scores_gemma":[0.9972255,0.0004890152,0.0002711407,0.0006267952,0.0005304628,0.0008570781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006874394,0.002845068,0.01418227,0.0002120432,0.0000175387,0.0009544355,0.00286377,0.002510167,0.03278787,0.008645683,0.03647784,0.8978159],"study_design_scores_gemma":[0.0002681111,0.006739947,0.1110774,0.0002763927,0.0000602969,0.003231172,0.005764966,0.01770596,0.04453032,0.008336827,0.8018314,0.0001770843],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5529396,0.0008033066,0.1121188,0.005698718,0.0005212347,0.00453387,0.0005199389,0.0106208,0.3122438],"genre_scores_gemma":[0.6913864,0.0006181724,0.08229148,0.0007852156,0.0003496773,0.001220345,0.0007851834,0.0005134133,0.2220501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05025759,"threshold_uncertainty_score":0.1681284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1364759749326002,"score_gpt":0.481546720097201,"score_spread":0.3450707451646008,"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."}}