{"id":"W2057747623","doi":"10.5430/jbar.v4n1p16","title":"The Improved Function and Commercial Design of the Intelligent Fitting System","year":2015,"lang":"en","type":"article","venue":"Journal of Business Administration Research","topic":"Advanced Technology in Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Field (mathematics); Artificial intelligence; Function (biology); Expert system; Intelligent decision support system; Marketing and artificial intelligence; Robot; Human–computer interaction; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001023383,0.0006406869,0.0004760043,0.0009326369,0.0009132248,0.001836484,0.001393488,0.001484253,0.007982261],"category_scores_gemma":[0.001897648,0.0003633955,0.0006211098,0.0007113198,0.0005189372,0.002899968,0.0006950976,0.0007674028,0.002912711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009906287,"about_ca_system_score_gemma":0.0008464484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001961659,"about_ca_topic_score_gemma":0.0008172531,"domain_scores_codex":[0.9989845,0.0002284782,0.00007558671,0.000218762,0.000405626,0.00008694379],"domain_scores_gemma":[0.9991595,0.00006960936,0.00004336151,0.0001187927,0.0005674302,0.00004135076],"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":[0.0005289064,0.0003259255,0.003858218,0.0005043693,0.00005753334,0.0007499796,0.0009502164,0.03902389,0.1233827,0.1666716,0.01635291,0.6475939],"study_design_scores_gemma":[0.000179508,0.001414264,0.005504321,0.0001017761,0.0002361038,0.001862415,0.0003721099,0.657159,0.09580199,0.02170864,0.2154313,0.0002285848],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05036526,0.000989688,0.8797287,0.001347598,0.0003802095,0.0003709412,0.00009427116,0.00384805,0.06287524],"genre_scores_gemma":[0.6685907,0.0007956609,0.292633,0.000397743,0.0002290035,0.0003588884,0.0002222522,0.0003817881,0.03639108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007982261,"threshold_uncertainty_score":0.0267033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1458519338897708,"score_gpt":0.3735861951945273,"score_spread":0.2277342613047565,"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."}}