{"id":"W3189109704","doi":"","title":"Learning to Elect","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Voting; Computer science; Minimax; Artificial neural network; Artificial intelligence; Set (abstract data type); Machine learning; Product (mathematics); Cardinal voting systems; Theoretical computer science; Data mining; Mathematical optimization; 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":[0.001518928,0.0004285935,0.0006370883,0.0007182576,0.0005247174,0.00117689,0.000802792,0.0009641974,0.009507332],"category_scores_gemma":[0.01067767,0.0003035137,0.000518801,0.0006839946,0.0009055518,0.001938486,0.0007560808,0.001425565,0.001668768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00077221,"about_ca_system_score_gemma":0.000841359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001359337,"about_ca_topic_score_gemma":0.003443562,"domain_scores_codex":[0.9991278,0.0003414263,0.00004951081,0.0002720548,0.0001128676,0.00009628275],"domain_scores_gemma":[0.9969818,0.002089495,0.0002255801,0.0003744764,0.0002255797,0.0001031163],"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":[0.0002267524,0.0002185904,0.01286572,0.0002395428,0.0001503252,0.0001061327,0.0003154466,0.3194249,0.001651695,0.2884011,0.01882115,0.3575786],"study_design_scores_gemma":[0.00004058236,0.00005414344,0.001138241,0.00003563854,0.00002207123,0.00005972119,0.00009885086,0.6546224,0.001050777,0.3383167,0.004546118,0.00001476491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.163597,0.0004383959,0.7973208,0.003414314,0.0002240239,0.0001831648,0.0009456518,0.0006339477,0.03324278],"genre_scores_gemma":[0.9130796,0.0002377195,0.07251362,0.0004408339,0.00008339705,0.0001691128,0.001082189,0.00009593349,0.01229775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009507332,"threshold_uncertainty_score":0.03180516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03824880718062647,"score_gpt":0.179891484794872,"score_spread":0.1416426776142456,"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."}}