{"id":"W2952487561","doi":"","title":"Toward Experiential Utility Elicitation for Interface Customization","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Personalization; Preference elicitation; Computer science; Experiential learning; Requirements elicitation; Interface (matter); Human–computer interaction; Presentation (obstetrics); User interface; Expert elicitation; Quality (philosophy); Domain (mathematical analysis); Ask price; Software; Preference; Knowledge management; World Wide Web; Requirements engineering; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001239322,0.0002390568,0.0002975853,0.0004197439,0.0001869616,0.000286002,0.0009837165,0.0002400893,0.0007219075],"category_scores_gemma":[0.0003463303,0.0002412616,0.0002981777,0.0004699808,0.0001023461,0.001153421,0.000713772,0.0002107576,0.0003691134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00014319,"about_ca_system_score_gemma":0.00006908568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003633662,"about_ca_topic_score_gemma":0.00001837321,"domain_scores_codex":[0.9981577,0.000136871,0.0004706617,0.0006489724,0.0002988594,0.0002869566],"domain_scores_gemma":[0.9978496,0.0002338062,0.0005287966,0.0006497343,0.000597453,0.000140588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003981184,0.001513107,0.09924091,0.0006608648,0.0006048206,0.00003288518,0.0515361,0.4000117,0.0003997847,0.1652496,0.1955533,0.08121575],"study_design_scores_gemma":[0.001801912,0.00006543693,0.01849489,0.000056892,0.0003448751,0.000001023486,0.01009839,0.8645664,0.0006042075,0.02546228,0.0775251,0.0009785391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4775852,0.00002042691,0.518786,0.00008252772,0.001052065,0.0004815955,0.00006392189,0.00007256742,0.001855773],"genre_scores_gemma":[0.9945068,0.00001510201,0.000499573,0.0000692879,0.0001296824,0.000006671784,0.0001300346,0.00001261909,0.004630279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5182863,"threshold_uncertainty_score":0.9838364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5183484482689067,"score_gpt":0.3558588631991785,"score_spread":0.1624895850697282,"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."}}