{"id":"W3087642349","doi":"10.3390/jrfm13090220","title":"Mapping the Scientific Research on Mass Customization Domain: A Critical Review and Bibliometric Analysis","year":2020,"lang":"en","type":"review","venue":"Journal of risk and financial management","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mass customization; Data science; Personalization; Domain (mathematical analysis); Computer science; Field (mathematics); Analytics; Identification (biology); Visualization; Domain analysis; Bibliometrics; Software; World Wide Web; Software development; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.006201348,0.000292737,0.001252442,0.04663859,0.0008160313,0.001092172,0.0004550096,0.0001072558,0.00003133329],"category_scores_gemma":[0.00142217,0.0001865908,0.0003119815,0.1261257,0.0001879805,0.0005168249,0.000355224,0.0006759331,0.00004428032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007834771,"about_ca_system_score_gemma":0.00007620609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004369756,"about_ca_topic_score_gemma":0.000002362816,"domain_scores_codex":[0.9971372,0.0001851219,0.0009565722,0.0004618688,0.0009409834,0.0003182488],"domain_scores_gemma":[0.9979972,0.0002528729,0.0008806398,0.0002650704,0.000564698,0.00003947242],"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":[0.00001252175,0.00003912074,0.00008372828,0.01723428,0.0001987686,0.00004063012,0.00003508681,0.000002386026,1.5361e-8,0.005241425,0.01411548,0.9629965],"study_design_scores_gemma":[0.0001991963,0.00001459431,0.0008586782,0.005748736,0.004529223,0.000004646845,0.0000758914,0.00002542563,1.311644e-8,0.001460777,0.9868809,0.0002019133],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001879353,0.9914822,0.005446624,0.001027067,0.0004589052,0.0007340378,0.000005010863,0.00001327731,0.000814079],"genre_scores_gemma":[0.0002884019,0.9978405,0.0004162837,0.0003689064,0.0009212658,0.00002739223,0.00002141635,0.00002294669,0.0000929314],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9727654,"threshold_uncertainty_score":0.9999448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08367487445526799,"score_gpt":0.3314101438562855,"score_spread":0.2477352694010175,"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."}}