{"id":"W3195176773","doi":"10.3390/e23091105","title":"Memory and Markov Blankets","year":2021,"lang":"en","type":"article","venue":"Entropy","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Office of Naval Research; Engineering and Physical Sciences Research Council; Fonds National de la Recherche Luxembourg; Rosetrees Trust; Social Sciences and Humanities Research Council of Canada; Wellcome Trust; Wellcome","keywords":"Markov blanket; Blanket; Markov chain; Independence (probability theory); Computer science; Statistical physics; Set (abstract data type); Conditional independence; Argument (complex analysis); State (computer science); Simple (philosophy); Markov model; Mathematics; Markov property; Artificial intelligence; Algorithm; Epistemology; Statistics; Physics; 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":[],"consensus_categories":[],"category_scores_codex":[0.00002774073,0.00004528455,0.0000491824,0.00001425087,0.00006033595,0.00003744351,0.00003341739,0.00001712046,0.0002189472],"category_scores_gemma":[0.0001520413,0.00003985232,0.0000179237,0.00007228988,0.00002587287,0.00005137517,0.00004238156,0.00005393769,0.00004774306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007614536,"about_ca_system_score_gemma":0.000009946166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001764594,"about_ca_topic_score_gemma":0.000001587509,"domain_scores_codex":[0.9995329,0.00004132079,0.00005248056,0.0001858785,0.0000860108,0.0001013443],"domain_scores_gemma":[0.9997748,0.00006460731,0.00001571757,0.00009702468,0.000008925217,0.00003897297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008829466,0.00001752291,0.0002481969,0.000004903671,0.00000100479,0.0001142299,0.00002154355,0.00000237042,0.9816215,0.007447182,0.001491797,0.009020942],"study_design_scores_gemma":[0.0005852393,0.00005888804,0.01049567,0.000009969413,0.000008663534,0.0002362091,0.00003649773,0.004693056,0.9482835,0.004502004,0.03091295,0.0001773639],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898597,0.00005784988,0.0001261214,0.001507563,0.0004964601,0.00004004943,0.000004365923,0.00003683426,0.00787103],"genre_scores_gemma":[0.9925677,0.00007407778,0.00009892054,0.001736426,0.00007544619,0.000001872057,0.000001599249,0.000005606388,0.005438345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.033338,"threshold_uncertainty_score":0.2397319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446114951910778,"score_gpt":0.2307510812527944,"score_spread":0.2162899317336866,"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."}}