{"id":"W1979383090","doi":"10.1109/icassp.2014.6854782","title":"Probabilistic ranking of multi-attribute items using indifference curve","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Computer science; Rank (graph theory); Probabilistic logic; Preference; Process (computing); Quality (philosophy); Selection (genetic algorithm); Machine learning; Competition (biology); Data mining; Information retrieval; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000464212,0.0001018393,0.0001581118,0.00009598712,0.00006218005,0.0001276929,0.0008990451,0.00002650703,0.00001281599],"category_scores_gemma":[0.00009500318,0.00008387988,0.00003626063,0.0003188496,0.00004160286,0.0005506725,0.0004873614,0.00006053606,0.00001659457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000144123,"about_ca_system_score_gemma":0.00001429963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000800809,"about_ca_topic_score_gemma":0.000009854824,"domain_scores_codex":[0.9990093,0.00005881078,0.0002171632,0.0002867149,0.0002165747,0.0002114191],"domain_scores_gemma":[0.9992042,0.00008442534,0.0001083663,0.0005010483,0.0000596149,0.00004238036],"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":[0.000008350159,0.0008751508,0.0852536,0.0005957588,0.0001552366,0.00001902433,0.002994803,0.002257785,0.007239368,0.6116118,0.0009731679,0.2880159],"study_design_scores_gemma":[0.000370218,0.00003324612,0.008397569,0.00003715713,0.000007469441,0.000001532006,0.000009165215,0.9869425,0.0008935567,0.002779857,0.0003753493,0.0001524072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05702953,0.00001266085,0.9421382,0.00005253783,0.0002264752,0.0001402539,0.000002935158,0.00008548664,0.0003119314],"genre_scores_gemma":[0.8450575,0.000001948969,0.154593,0.00006481403,0.00003234277,0.000003011638,0.000005687821,0.000004576051,0.0002371108],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9846847,"threshold_uncertainty_score":0.3420522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05466592958831019,"score_gpt":0.2721744805885767,"score_spread":0.2175085510002666,"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."}}