{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01165094,0.001168512,0.0006772194,0.001795498,0.0006808505,0.003374984,0.000701837,0.0007151128,0.001228069],"category_scores_gemma":[0.01927357,0.0005467489,0.0007004574,0.0008016297,0.001625678,0.003104259,0.001842431,0.0009420987,0.0004031538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007123924,"about_ca_system_score_gemma":0.001204869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009091058,"about_ca_topic_score_gemma":0.001461304,"domain_scores_codex":[0.9871678,0.009123089,0.000791455,0.0004628498,0.002173188,0.0002816573],"domain_scores_gemma":[0.9825869,0.01204172,0.0006856056,0.0006711458,0.003807674,0.0002069493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000679309,0.0006033006,0.02204262,0.005247929,0.0003217189,0.0005208952,0.07010411,0.004747851,0.1187854,0.01736166,0.001935265,0.75765],"study_design_scores_gemma":[0.0005330396,0.01935991,0.22067,0.006561355,0.002106548,0.01069571,0.1515682,0.140481,0.1943246,0.08805417,0.1638094,0.001835987],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2710588,0.001975817,0.7101064,0.0007583516,0.0001086475,0.0009183308,0.00005129895,0.0009915816,0.01403088],"genre_scores_gemma":[0.6292017,0.0008369695,0.3671826,0.0002008007,0.00002931838,0.0005919785,0.00005732016,0.0001334924,0.001765821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01165094,"threshold_uncertainty_score":0.06161678,"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."}}