{"id":"W58252060","doi":"10.1007/978-3-642-24873-3_8","title":"Efficiently Eliciting Preferences from a Group of Users","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Group (periodic table); Human–computer interaction","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":[0.005334406,0.001018125,0.001602318,0.0007757631,0.0005011129,0.001707903,0.001108561,0.001288265,0.003457287],"category_scores_gemma":[0.01656441,0.0004557727,0.001035377,0.001342365,0.0003656379,0.002486313,0.002148824,0.001246815,0.001608005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004816683,"about_ca_system_score_gemma":0.001123784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001186676,"about_ca_topic_score_gemma":0.002062459,"domain_scores_codex":[0.9930976,0.00433587,0.0002828569,0.0005668456,0.001268252,0.0004485599],"domain_scores_gemma":[0.9857726,0.0109941,0.000481805,0.00135947,0.001015819,0.0003762639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008938744,0.001970122,0.0263455,0.0009479928,0.0005718325,0.0005088365,0.002143015,0.0868343,0.09112646,0.01659801,0.01143946,0.7525758],"study_design_scores_gemma":[0.0005654265,0.0020804,0.009100384,0.00007247557,0.000237826,0.0004736961,0.002648161,0.8792475,0.03946944,0.05999949,0.005983396,0.0001217672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3239814,0.0002482152,0.6662787,0.0006643798,0.00003869006,0.0005184222,0.0007672818,0.0009837906,0.006519143],"genre_scores_gemma":[0.750466,0.0001596377,0.2437752,0.0001987288,0.00005751185,0.0006253354,0.001548001,0.00009199706,0.00307751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005334406,"threshold_uncertainty_score":0.02821136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02781100083978531,"score_gpt":0.2309118307609883,"score_spread":0.203100829921203,"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."}}