{"id":"W2961010205","doi":"10.1007/s10827-019-00721-9","title":"Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hôpital du Sacré-Cœur de Montréal; Concordia University; École de Technologie Supérieure; McGill University; Canadian Sleep & Circadian Network; Montreal Neurological Institute and Hospital","funders":"Agence Nationale de la Recherche","keywords":"Electroencephalography; Scaling; Coherence (philosophical gambling strategy); Magnetoencephalography; Amplitude; Neuroimaging; Local field potential; Logarithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006375906,0.0001701131,0.0003384267,0.0002045768,0.000249041,0.0001729882,0.0002231943,0.00003453524,0.000006020433],"category_scores_gemma":[0.002557872,0.0001499853,0.00003153908,0.0003927099,0.0003451308,0.000744649,0.000121084,0.0002769041,0.000001410445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004340081,"about_ca_system_score_gemma":0.0000837957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000418809,"about_ca_topic_score_gemma":0.00001007696,"domain_scores_codex":[0.9977779,0.0001602936,0.0005103843,0.0004656711,0.0008750704,0.0002106123],"domain_scores_gemma":[0.9924117,0.006876652,0.0003899731,0.00008115734,0.0001415882,0.00009894165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00010972,0.0003012303,0.7918206,0.00002393436,0.000007189438,0.0000395777,0.001222184,0.123146,0.0816677,0.001299999,0.00001034395,0.0003515449],"study_design_scores_gemma":[0.001253393,0.001142815,0.8274354,0.00007033075,0.0000103155,0.00004512595,0.0002464761,0.1632093,0.0007683894,0.005612197,0.000008468261,0.0001978775],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938726,0.00005108399,0.004681773,0.000696738,0.00032732,0.0003126248,0.00001162927,0.00001361857,0.00003258991],"genre_scores_gemma":[0.9991725,0.000004039771,0.0001992286,0.0005787913,0.00002799199,0.00000312858,2.877622e-7,0.00000880125,0.000005216852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08089931,"threshold_uncertainty_score":0.6116222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03966869608664201,"score_gpt":0.2902784136519733,"score_spread":0.2506097175653313,"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."}}