{"id":"W3164386808","doi":"10.1101/2021.05.26.445816","title":"Maximally predictive ensemble dynamics from data","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Okinawa Institute of Science and Technology Graduate University","keywords":"Computer science; Statistical physics; Operator (biology); Parameterized complexity; Leverage (statistics); Langevin dynamics; Scalability; Algorithm; Artificial intelligence; 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":[0.002401422,0.001080983,0.001282686,0.001311658,0.0005610211,0.001647119,0.001609192,0.001424622,0.001606434],"category_scores_gemma":[0.01071144,0.0007526855,0.001091806,0.0009188202,0.00185085,0.003350178,0.002245225,0.002468547,0.0002844087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001358951,"about_ca_system_score_gemma":0.001002196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003856195,"about_ca_topic_score_gemma":0.004444468,"domain_scores_codex":[0.9990761,0.0003636244,0.00005232505,0.0002480635,0.0001768874,0.00008305894],"domain_scores_gemma":[0.9932787,0.004896831,0.0005869256,0.0006830679,0.0003673001,0.0001871797],"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":[0.00004942223,0.00003266703,0.001472167,0.00006049647,0.00005977233,0.00006483284,0.0001127325,0.9423,0.000877964,0.03951238,0.0006752152,0.01478234],"study_design_scores_gemma":[0.000001512077,0.000005795755,0.00007215854,0.000003783041,0.000002140278,0.000003594653,0.000002927953,0.9863601,0.0001063051,0.01334936,0.00008895659,0.000003413452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06516309,0.00041526,0.9308457,0.000875395,0.00004988395,0.00004464091,0.0003615317,0.0004576472,0.001786871],"genre_scores_gemma":[0.8947858,0.0003790293,0.1004827,0.0003250616,0.0001490666,0.0001879419,0.001249209,0.0001555058,0.002285628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003856195,"threshold_uncertainty_score":0.01270008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168767828921303,"score_gpt":0.231902384785387,"score_spread":0.200214706496174,"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."}}