{"id":"W2896386286","doi":"10.1016/j.neuroimage.2018.10.034","title":"Measuring transient phase-amplitude coupling using local mutual information","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health","keywords":"Estimator; Computer science; Coupling (piping); Amplitude; Mutual information; Set (abstract data type); Measure (data warehouse); Event (particle physics); Data set; Transient (computer programming); Artificial intelligence; Data mining; Mathematics; Physics; Statistics","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":[0.0001518103,0.0001473253,0.0001185227,0.0001194666,0.0003250135,0.0001624234,0.0001641131,0.00004393795,0.00005086944],"category_scores_gemma":[0.0001740203,0.0001424361,0.00006072404,0.0002802587,0.0001800569,0.0009045572,0.00004889195,0.0001806946,0.0001735456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005423362,"about_ca_system_score_gemma":0.00003025912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001447904,"about_ca_topic_score_gemma":0.000003433427,"domain_scores_codex":[0.9987165,0.00003590928,0.000275819,0.0002913308,0.0003668737,0.0003135488],"domain_scores_gemma":[0.9994636,0.00007224802,0.00009221747,0.0002149663,0.00006903715,0.00008796548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000822318,0.00005418535,0.00002139751,0.00001416847,0.000001215628,0.00001294079,0.0001714773,0.001383967,0.986883,0.0003461375,0.00007108758,0.01095823],"study_design_scores_gemma":[0.0009065969,0.0002927149,0.0004365691,0.0000173334,0.00001122766,0.00007439536,0.00002905477,0.681693,0.3101089,0.00007309871,0.006167278,0.0001898653],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8462479,0.000002119166,0.1510324,0.00009527607,0.0008100037,0.0001780735,0.00001430989,0.000132304,0.001487697],"genre_scores_gemma":[0.9983706,0.000003609666,0.0001954238,0.001205441,0.000171066,0.000003415125,0.000004180964,0.00001750304,0.00002868775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.680309,"threshold_uncertainty_score":0.5808376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08859852455801005,"score_gpt":0.2948699387217416,"score_spread":0.2062714141637315,"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."}}