{"id":"W4293230413","doi":"10.3389/fncir.2022.834434","title":"Splitting of the magnetic encephalogram into «brain» and «non-brain» physiological signals based on the joint analysis of frequency-pattern functional tomograms and magnetic resonance images","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neural Circuits","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Higher Education of the Russian Federation; Russian Foundation for Basic Research; Moscow Center of Fundamental and Applied Mathematics","keywords":"Noise (video); Nuclear magnetic resonance; Functional magnetic resonance imaging; Magnetic resonance imaging; Human head; Radio spectrum; Human brain; Physics; Computer science; Acoustics; Artificial intelligence; Neuroscience; Psychology; Telecommunications; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006935937,0.0002263399,0.0004592224,0.0002972407,0.0004556517,0.00003046972,0.000362031,0.00004611288,0.0001147209],"category_scores_gemma":[0.00428772,0.0001538604,0.0001812718,0.001628371,0.001011795,0.00007991825,0.0003263587,0.0004189232,3.018689e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006330729,"about_ca_system_score_gemma":0.00003779396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009499832,"about_ca_topic_score_gemma":0.0000192889,"domain_scores_codex":[0.9970972,0.0008653477,0.0004122779,0.0006919003,0.0006322634,0.0003010053],"domain_scores_gemma":[0.9951161,0.004168635,0.0002294075,0.0003893582,0.00005204122,0.00004443359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001142239,0.0003680172,0.4414713,0.000105532,0.00008312801,0.00002750604,0.001317245,0.01117687,0.4591588,0.0002306997,0.008558897,0.07738785],"study_design_scores_gemma":[0.0004975044,0.000811577,0.9248635,0.00003168838,0.00008593255,0.000005901069,0.0006511647,0.06721,0.003321745,0.002244089,0.00008012613,0.0001968203],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880273,0.001501575,0.0002731258,0.00904514,0.0003905791,0.0005114007,0.00008531955,0.00002044433,0.0001450539],"genre_scores_gemma":[0.993952,0.00001402092,0.00004417726,0.005734291,0.0000269802,0.0001449021,0.000003053451,0.00001295393,0.00006759185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4833922,"threshold_uncertainty_score":0.6274245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02804918380474299,"score_gpt":0.2271133583032567,"score_spread":0.1990641744985137,"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."}}