{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000180119,0.0000468127,0.0001052835,0.0001673669,0.0001306084,0.00003536791,0.0001534931,0.00003732343,0.3363991],"category_scores_gemma":[0.0009265142,0.00003667356,0.00005740482,0.0008264725,0.0000223078,0.0001162549,0.00007856994,0.00002584263,0.0008881295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003962783,"about_ca_system_score_gemma":0.00001304763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001799488,"about_ca_topic_score_gemma":0.000006739784,"domain_scores_codex":[0.9992234,0.00004384276,0.0001970716,0.0002048547,0.0002700307,0.0000608142],"domain_scores_gemma":[0.9990296,0.0001825096,0.000133751,0.0002294521,0.0003789084,0.00004580486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001715171,0.0009939132,0.002111795,0.0000571563,0.0009373982,0.000004828921,0.004017869,0.3979374,0.01266546,0.01841014,0.4801868,0.08250567],"study_design_scores_gemma":[0.000317754,0.00006514514,0.01336166,0.00005470945,0.00009918112,0.000004871164,0.0004132859,0.9514986,0.01389984,0.00798546,0.01209659,0.0002028933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01617917,0.0001198,0.7916149,0.0004184329,0.0001653989,0.000616768,0.1870559,0.0001526907,0.00367695],"genre_scores_gemma":[0.9735366,3.664249e-7,0.01964251,0.00009826061,0.00003476697,0.0000250585,0.005334683,0.00000458167,0.001323151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9573575,"threshold_uncertainty_score":0.9998898,"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."}}