{"id":"W1595573567","doi":"10.48550/arxiv.1206.3258","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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Personalization; Preference elicitation; Experiential learning; Computer science; Requirements elicitation; Interface (matter); Human–computer interaction; User interface; Presentation (obstetrics); Expert elicitation; Domain (mathematical analysis); Quality (philosophy); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02511247,0.001592599,0.0006315886,0.001861252,0.0006996288,0.003331284,0.00207773,0.001593255,0.002790435],"category_scores_gemma":[0.1019065,0.0009197114,0.0008727941,0.001648398,0.00294329,0.005021826,0.004330832,0.002634602,0.0005443899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002042515,"about_ca_system_score_gemma":0.002359826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00160603,"about_ca_topic_score_gemma":0.002089067,"domain_scores_codex":[0.9725406,0.02170719,0.001107988,0.001420748,0.002883974,0.0003394283],"domain_scores_gemma":[0.904505,0.07439693,0.004954165,0.01020508,0.005162632,0.000776163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005850092,0.001822946,0.02086075,0.001335298,0.0002306982,0.000262831,0.01373424,0.1136815,0.01848217,0.4521625,0.003048353,0.3737937],"study_design_scores_gemma":[0.0001283778,0.0003332697,0.005054376,0.0003022882,0.00004187135,0.0001649592,0.002072668,0.4892518,0.01260357,0.4821761,0.007738499,0.0001322635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02241095,0.00005702052,0.9736423,0.0005190388,0.000007411752,0.000284403,0.0001035439,0.0001750383,0.002800264],"genre_scores_gemma":[0.3180036,0.0001459867,0.6799548,0.0001837352,0.00001704513,0.0009267097,0.0002147673,0.00003669515,0.0005167191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02511247,"threshold_uncertainty_score":0.132809,"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."}}