{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001351833,0.0003098842,0.000517755,0.0008713201,0.0003209882,0.003364859,0.001540521,0.0001172166,0.003086305],"category_scores_gemma":[0.0002494053,0.0002915652,0.0001523516,0.00103271,0.00003725535,0.00788737,0.0004927598,0.0002249737,0.00009215668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009245813,"about_ca_system_score_gemma":0.00006807222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001396484,"about_ca_topic_score_gemma":0.000009361323,"domain_scores_codex":[0.9977991,0.00002803207,0.0006925476,0.0005000315,0.0005461936,0.0004341225],"domain_scores_gemma":[0.9981245,0.00007841441,0.0009755758,0.0003360885,0.0004475339,0.00003790352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004676477,0.0002316654,0.7895152,0.0008326668,0.0002405123,0.000005423009,0.0001317827,0.004475106,0.02413535,0.001316575,0.06623549,0.1124126],"study_design_scores_gemma":[0.003430955,0.00001291021,0.5195538,0.0009710144,0.0003533479,0.00000373563,0.0003499505,0.01409595,0.02516456,0.01977348,0.4144544,0.00183598],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9524614,0.001789125,0.006809593,0.0006861494,0.003666184,0.004372707,0.000007643526,0.0003683938,0.02983878],"genre_scores_gemma":[0.994393,0.0003778818,0.002095166,0.0007236538,0.001012319,0.0001457693,0.0001117787,0.00009463572,0.001045835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3482189,"threshold_uncertainty_score":0.9999536,"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."}}