{"id":"W2796533672","doi":"10.1101/299859","title":"Parameterizing neural power spectra","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":181,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; Whitehall Foundation; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; University of California, San Diego; National Science Foundation","keywords":"Aperiodic graph; A priori and a posteriori; Computer science; Oscillation (cell signaling); Narrowband; Population; Spectral density; Algorithm; SIGNAL (programming language); Completeness (order theory); Mathematics; Telecommunications; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001569178,0.0008328569,0.000539999,0.001533302,0.0003803933,0.001211961,0.001161295,0.0008907379,0.002364516],"category_scores_gemma":[0.00954558,0.0003609428,0.0005486719,0.0007816837,0.0006260945,0.001261587,0.00103505,0.0009951977,0.0007401271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005397471,"about_ca_system_score_gemma":0.0009018613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002146205,"about_ca_topic_score_gemma":0.002246947,"domain_scores_codex":[0.9993957,0.0001509047,0.0000490998,0.0001975428,0.0001633152,0.00004344587],"domain_scores_gemma":[0.9973883,0.001659398,0.0002332587,0.0002903303,0.0003827038,0.00004600277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001941611,0.0001116624,0.006702827,0.0002102696,0.0001196669,0.000105882,0.0003247405,0.4009648,0.03688204,0.0164137,0.002050286,0.53592],"study_design_scores_gemma":[0.000006420144,0.00001611231,0.0009957504,0.00001173597,0.000006272319,0.00003552589,0.00002528439,0.9849999,0.005604398,0.007378229,0.0009094758,0.00001094849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02061155,0.00005853658,0.9776146,0.00004310244,0.00001113387,0.00004675826,0.00008051874,0.0009942495,0.0005395403],"genre_scores_gemma":[0.2436938,0.00008307305,0.7541272,0.00004905167,0.00001781159,0.0002570176,0.0004818179,0.0003072553,0.0009829365],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002364516,"threshold_uncertainty_score":0.008298755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0241181047702891,"score_gpt":0.2317852461257226,"score_spread":0.2076671413554335,"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."}}