{"id":"W2322280599","doi":"10.1097/wnp.0b013e3182121843","title":"Realignment of Magnetoencephalographic Data for Group Analysis in the Sensor Domain","year":2011,"lang":"en","type":"article","venue":"Journal of Clinical Neurophysiology","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"","keywords":"Magnetoencephalography; Computer science; Smoothing; Sensor array; Artificial intelligence; Pattern recognition (psychology); Algorithm; Computer vision; Electroencephalography; Machine learning","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":[0.00124317,0.001195641,0.0006556479,0.001031616,0.0003384449,0.0009354397,0.0008711453,0.0008068956,0.003588835],"category_scores_gemma":[0.00636677,0.0003774371,0.00107225,0.0009482919,0.0005206805,0.0009203851,0.0009427947,0.001155919,0.002276209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002615686,"about_ca_system_score_gemma":0.000511913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006558554,"about_ca_topic_score_gemma":0.001051706,"domain_scores_codex":[0.9992429,0.0001976986,0.00006629335,0.0001910304,0.0002691069,0.00003297152],"domain_scores_gemma":[0.9985747,0.000554961,0.0001161946,0.0004829498,0.0002369793,0.00003411869],"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":[0.0004140914,0.0002036985,0.001282801,0.0002539503,0.0002299251,0.0002189579,0.0003629112,0.06512415,0.1147604,0.01650807,0.00372025,0.7969207],"study_design_scores_gemma":[0.00005750948,0.0004378912,0.004356347,0.00003779968,0.0001052222,0.0007832907,0.0001599518,0.8247334,0.1171223,0.02716847,0.02492916,0.0001085672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006922133,0.00004896189,0.9919794,0.00005942729,0.00005905684,0.00004075437,0.00004877245,0.0006334723,0.0002080784],"genre_scores_gemma":[0.04916762,0.0001290086,0.9488412,0.00004808052,0.00003803663,0.0001144562,0.0003224204,0.0003650557,0.000974237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003588835,"threshold_uncertainty_score":0.01200581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.198168636263262,"score_gpt":0.3813134728464709,"score_spread":0.183144836583209,"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."}}