{"id":"W318376482","doi":"10.1184/r1/6603437","title":"Analysis and Optimization of Multi-dimensional Percentile Mechanisms","year":2018,"lang":"en","type":"article","venue":"Figshare","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Percentile; Mathematical optimization; Computer science; Selection (genetic algorithm); Sample (material); Class (philosophy); Mechanism (biology); Mechanism design; Value (mathematics); Mathematics; Mathematical economics; Artificial intelligence; Statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01029744,0.001279635,0.002004718,0.001365711,0.0006545824,0.003011131,0.002609418,0.001834023,0.004014832],"category_scores_gemma":[0.02560842,0.0009086463,0.001311322,0.001658735,0.00160691,0.004477504,0.001962977,0.002076844,0.0003883964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002089497,"about_ca_system_score_gemma":0.001521794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007874877,"about_ca_topic_score_gemma":0.0006472329,"domain_scores_codex":[0.9949746,0.003339804,0.0001456978,0.000395105,0.0006760143,0.0004689287],"domain_scores_gemma":[0.9868939,0.009137081,0.001763426,0.001055692,0.0006507008,0.000499169],"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":[0.0001648301,0.0001029921,0.0008889097,0.000121509,0.0001089701,0.00008465377,0.00007690646,0.8332586,0.0008043086,0.1445398,0.001028246,0.01882038],"study_design_scores_gemma":[0.00003793995,0.0001225775,0.0002757424,0.00002377822,0.00002024696,0.0000601824,0.00004136457,0.8914399,0.0003706935,0.1068363,0.0007534641,0.0000178764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05882899,0.0006516619,0.9350366,0.0005283611,0.00003456245,0.0001180332,0.000146299,0.0001643939,0.004491013],"genre_scores_gemma":[0.8742805,0.0007371434,0.1207526,0.0001674068,0.0000598518,0.0003934719,0.0001837375,0.00006854619,0.003356771],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01029744,"threshold_uncertainty_score":0.05445868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070639558716939,"score_gpt":0.3701842611721028,"score_spread":0.263120305300409,"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."}}