{"id":"W3126389217","doi":"10.1109/tcyb.2021.3049769","title":"Variational Learning Data Fusion With Unknown Correlation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Divergence (linguistics); Mathematics; Correlation; Sensor fusion; Bayesian probability; Positive-definite matrix; Joint probability distribution; Identification (biology); Posterior probability; Mean squared error; Fusion; Pattern recognition (psychology); Correlation coefficient; Artificial intelligence; Algorithm; Computer science; Statistics; Eigenvalues and eigenvectors; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003159163,0.0001389531,0.0001089719,0.0001259343,0.0009357228,0.0001147428,0.0009629691,0.00004921401,0.000355679],"category_scores_gemma":[0.000005901821,0.0001356165,0.00003020132,0.0005588767,0.0000322937,0.0003242998,0.00003298345,0.0007078648,0.00005120449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006604846,"about_ca_system_score_gemma":0.00008279022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003524067,"about_ca_topic_score_gemma":0.00002283426,"domain_scores_codex":[0.9982347,0.0001774979,0.0002148088,0.0005115278,0.0006446544,0.0002168229],"domain_scores_gemma":[0.9986018,0.0002588872,0.0001056251,0.000895697,0.00006260927,0.00007535912],"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":[0.00003034509,0.0001557771,0.00004697972,0.000002005017,0.00001625421,0.000009158292,0.0002703567,0.951058,0.00004276309,0.003901013,0.00134008,0.04312731],"study_design_scores_gemma":[0.0004142032,0.0002581827,0.0002282828,0.00001189004,0.0000209634,0.00005876004,0.00004123923,0.9327832,0.00008217509,0.0002157786,0.0656921,0.0001931968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001531141,0.00002912936,0.9954388,0.0003757909,0.001259836,0.0001234235,0.00007629562,0.0002735404,0.000892052],"genre_scores_gemma":[0.9408234,0.00004400119,0.05668516,0.0002227228,0.00007137704,0.00002344157,0.0001919804,0.00002225618,0.001915677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9392923,"threshold_uncertainty_score":0.7196918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02291168167084333,"score_gpt":0.232990226146535,"score_spread":0.2100785444756917,"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."}}