{"id":"W2443212719","doi":"10.5539/mas.v10n7p208","title":"Integrating Usability in Automotive Navigation User Interface Design via Kansei Engineering","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Color perception and design","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Usability; Computer science; Automotive industry; Kansei; Human–computer interaction; Usability engineering; User interface; Kansei engineering; User interface design; Interface (matter); Usability inspection; Process (computing); User experience design; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001382444,0.0001456941,0.0001455249,0.0001678413,0.000101043,0.00004788001,0.000361964,0.00007629328,0.0004178835],"category_scores_gemma":[0.0001070068,0.0001093556,0.00002628092,0.0005402207,0.0002885498,0.0002270534,0.00006901285,0.0001642349,0.0004449287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003457632,"about_ca_system_score_gemma":0.00006428455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000046752,"about_ca_topic_score_gemma":0.00001528214,"domain_scores_codex":[0.9984314,0.00006218699,0.0002671422,0.0005900651,0.000257434,0.0003917136],"domain_scores_gemma":[0.9992282,0.0002029872,0.00006039896,0.0003432828,0.00006955011,0.00009557668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005425055,0.00006278315,0.0005752955,0.000003072177,0.000002217634,0.000001798298,0.007365567,0.0009189836,0.9317909,0.002076327,0.00003839076,0.05711045],"study_design_scores_gemma":[0.002647449,0.0003108009,0.1443595,0.0002470199,0.00001379792,0.00003449737,0.002115759,0.6876303,0.1452307,0.01576141,0.000462984,0.001185924],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3051484,0.00000444428,0.6918381,0.00009403226,0.0001608841,0.0002653382,9.908158e-7,0.0000895612,0.002398253],"genre_scores_gemma":[0.9907177,3.131746e-7,0.008757196,0.00007800751,0.00002244355,0.0001009755,3.347145e-7,0.00001131806,0.0003116962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7865602,"threshold_uncertainty_score":0.5718807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03077879204170396,"score_gpt":0.3080512478891275,"score_spread":0.2772724558474235,"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."}}