{"id":"W3106259297","doi":"","title":"1Nonanticipative Rate Distortion Function and Relations to Filtering Theory","year":2016,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mathematics; Infimum and supremum; Realizability; Realization (probability); RDF; Rate of convergence; Conditional probability distribution; Relation (database); Distribution (mathematics); Convergence (economics); Mathematical optimization; Applied mathematics; Distortion (music); Conditional expectation; Function (biology); Distribution function; Algorithm; Mathematical analysis; Computer science; Artificial intelligence; Channel (broadcasting); Data mining; Statistics","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":[0.000264685,0.00006002975,0.00005352353,0.00005293638,0.0001297632,0.00005235374,0.0001126221,0.0000268513,0.0001058498],"category_scores_gemma":[0.00007445071,0.00003721717,0.00001395119,0.0001250313,0.0000199507,0.0004018556,0.000119555,0.00003257401,0.0001536308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001598857,"about_ca_system_score_gemma":0.000006431114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000455997,"about_ca_topic_score_gemma":0.000004963631,"domain_scores_codex":[0.9994063,0.00007049649,0.0001085749,0.0002258054,0.0000688779,0.0001199037],"domain_scores_gemma":[0.9994018,0.0002371465,0.00002919954,0.0002301368,0.00003351597,0.00006821558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003801872,0.00002546764,0.003001426,0.000002597665,0.00001228264,0.000002761242,0.0005084603,0.0001563188,0.0111786,0.6379178,0.006338076,0.3408181],"study_design_scores_gemma":[0.001362263,0.0005488372,0.6638337,0.0002671122,0.00003652495,0.00002757723,0.0001134407,0.01887302,0.006695151,0.2406364,0.06663632,0.0009697382],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04140499,0.00001190484,0.9552892,0.0008560551,0.0003910439,0.00006099137,0.000002768236,0.000155375,0.001827605],"genre_scores_gemma":[0.9822774,0.00000638876,0.01463183,0.0002727195,0.00004790576,0.000008873128,0.000001513356,0.000003971947,0.002749421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9408724,"threshold_uncertainty_score":0.1974665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403503867750855,"score_gpt":0.2289556906337913,"score_spread":0.2149206519562827,"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."}}