{"id":"W4399199491","doi":"10.1145/3656650.3656683","title":"Flexible Visual Preference Inspection in Group Decision Making","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Preference; Group decision-making; Visualization; Computer science; Stakeholder; Human–computer interaction; Group (periodic table); User group; Decision support system; Decision analysis; Decision model; R-CAST; Artificial intelligence; Management science; Business decision mapping; Machine learning; Engineering; Psychology; Multimedia; Statistics; Mathematics","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":[0.009951258,0.001214359,0.001058153,0.001528839,0.0008158245,0.002783056,0.001642463,0.001061646,0.005852723],"category_scores_gemma":[0.02403893,0.0008037063,0.001059679,0.001087105,0.001365764,0.004097097,0.003359568,0.001678249,0.0006992362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007029058,"about_ca_system_score_gemma":0.0009175413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001348189,"about_ca_topic_score_gemma":0.001639184,"domain_scores_codex":[0.9942704,0.003886505,0.0002861212,0.000469189,0.0008282019,0.0002595496],"domain_scores_gemma":[0.9811725,0.01299518,0.001041978,0.002579207,0.001443297,0.0007678755],"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":[0.00328332,0.001187869,0.01167138,0.001550797,0.0002761425,0.001032014,0.02209488,0.1367639,0.08681691,0.09228389,0.007716313,0.6353226],"study_design_scores_gemma":[0.0004558346,0.0009317432,0.003776851,0.0003408596,0.0001168278,0.0004307737,0.002214831,0.8160739,0.03295572,0.1236376,0.01883798,0.0002271039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03683862,0.00006434934,0.9591767,0.0002690025,0.00002318681,0.0001888594,0.00007674268,0.001762562,0.00160001],"genre_scores_gemma":[0.3102464,0.00006109837,0.6882873,0.00007129763,0.00001162646,0.0002826053,0.0001048065,0.0002357469,0.0006991021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009951258,"threshold_uncertainty_score":0.05262792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04384971495418873,"score_gpt":0.3573642430944716,"score_spread":0.3135145281402829,"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."}}