{"id":"W3161817262","doi":"10.1103/physreve.104.024305","title":"Breakdown of random matrix universality in Markov models","year":2021,"lang":"en","type":"article","venue":"Physical review. E","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada; Novo Nordisk Fonden; Novo Nordisk","keywords":"Statistical physics; Universality (dynamical systems); Markov chain; Random matrix; Hidden Markov model; Stochastic matrix; Markov process; Computer science; Markov model; Leverage (statistics); Phase transition; Mathematics; Algorithm; Artificial intelligence; Eigenvalues and eigenvectors; Physics; Machine learning; Statistics; Quantum mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003151503,0.0005539797,0.001116027,0.001539973,0.0008974877,0.001920164,0.001337814,0.001244313,0.002903902],"category_scores_gemma":[0.02264202,0.0007954589,0.001002554,0.0004778142,0.004361918,0.004277349,0.00211704,0.002666336,0.0003746868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00177497,"about_ca_system_score_gemma":0.0007969623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003806034,"about_ca_topic_score_gemma":0.002031914,"domain_scores_codex":[0.9981915,0.0006801931,0.00006962202,0.000416131,0.0003506878,0.000291853],"domain_scores_gemma":[0.9868585,0.008868606,0.001336992,0.001683585,0.0005279821,0.0007243656],"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":[0.00006184544,0.00003170248,0.001437169,0.00009953357,0.00004663626,0.0001977795,0.0004940046,0.06362033,0.002460435,0.9239548,0.000843032,0.006752722],"study_design_scores_gemma":[0.00001213883,0.00001897923,0.0006205338,0.00001891992,0.000007513591,0.00007669281,0.00003416748,0.3255523,0.0003244491,0.6727415,0.0005703254,0.00002249339],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4249273,0.002509386,0.5478572,0.003036795,0.00009013171,0.0000665993,0.0003679152,0.001327098,0.01981772],"genre_scores_gemma":[0.9843791,0.0005101358,0.01337606,0.0001795362,0.0001283686,0.00007145171,0.0001563109,0.0001458349,0.001053151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003806034,"threshold_uncertainty_score":0.01666695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02803648618954196,"score_gpt":0.3234711825571614,"score_spread":0.2954346963676194,"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."}}