{"id":"W2931032518","doi":"10.48550/arxiv.1904.00875","title":"The Large Space of Information Structures","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Infimum and supremum; Combinatorics; Characterization (materials science); Metric space; Mathematics; Space (punctuation); Finite set; Discrete mathematics; Information structure; Stochastic game; Sequence (biology); Interpretation (philosophy); Computer science; Mathematical economics; Physics","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":[0.007780812,0.001153644,0.00211116,0.003557756,0.002780825,0.01270786,0.003103974,0.004427474,0.006231301],"category_scores_gemma":[0.03162306,0.001320265,0.001677973,0.003543644,0.006823525,0.02321961,0.005944306,0.005454751,0.0006755496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004849609,"about_ca_system_score_gemma":0.002233634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003069749,"about_ca_topic_score_gemma":0.002280262,"domain_scores_codex":[0.9902269,0.005048042,0.0004375339,0.001622446,0.002042692,0.0006223779],"domain_scores_gemma":[0.9730344,0.01885108,0.00205938,0.003644052,0.001287298,0.001123845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001553007,0.00001207084,0.0001864823,0.000032348,0.00001933836,0.00004454804,0.00009698826,0.007690168,0.0000673353,0.9882898,0.0005293466,0.003016009],"study_design_scores_gemma":[0.000006150557,0.000005402722,0.00005492455,0.0000137733,0.000005114556,0.00001548801,0.0000233617,0.02515971,0.00005239763,0.9736331,0.00102364,0.000007008719],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1530861,0.003987404,0.7718055,0.02466363,0.0002085437,0.0001234863,0.002462712,0.0006439988,0.04301865],"genre_scores_gemma":[0.8952478,0.002384623,0.09274703,0.001159809,0.0004254994,0.0003257197,0.0009592188,0.0001628042,0.006587502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01270786,"threshold_uncertainty_score":0.04114932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04800606582498089,"score_gpt":0.1842982524672706,"score_spread":0.1362921866422897,"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."}}