{"id":"W3137980132","doi":"10.1177/09544089211001776","title":"Definition of customer requirements in big data using word vectors and affinity propagation clustering","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering","topic":"Quality Function Deployment in Product Design","field":"Business, Management and Accounting","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Computer science; Cluster analysis; Word (group theory); Data mining; Artificial intelligence; Crawling; Product (mathematics); Big data; Natural language processing; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001944816,0.0002135266,0.0005281453,0.0004044918,0.00005088735,0.00004212881,0.0005163677,0.000139093,0.00001029108],"category_scores_gemma":[0.003570316,0.0001861961,0.0001056524,0.001199933,0.00005511453,0.001556255,0.0003761363,0.0003865128,5.183281e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008829693,"about_ca_system_score_gemma":0.000118413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001210456,"about_ca_topic_score_gemma":0.000002750739,"domain_scores_codex":[0.9974008,0.00001167023,0.001340839,0.0002796407,0.0007418852,0.0002251301],"domain_scores_gemma":[0.9975451,0.00006716996,0.001185467,0.0002009611,0.0009653574,0.00003600972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005330361,0.0007719191,0.001042233,0.008748097,0.000298992,0.000008556421,0.0001609368,0.06846662,0.7854125,0.1320731,0.00007259872,0.002411338],"study_design_scores_gemma":[0.003783585,0.0001540753,0.0006673697,0.007402755,0.0007699628,0.0001470482,0.0009165854,0.3889009,0.5842912,0.01153814,0.0006326178,0.0007957206],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9498845,0.0001882389,0.04719058,0.0003077389,0.001926053,0.0003397605,0.000007246016,0.00003843978,0.0001174442],"genre_scores_gemma":[0.993588,0.0000513732,0.005931881,0.00003392488,0.0003596834,0.00000413985,0.000003798781,0.00002532684,0.000001890978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3204343,"threshold_uncertainty_score":0.7592858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1046663106106878,"score_gpt":0.270487747503902,"score_spread":0.1658214368932142,"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."}}