{"id":"W2155435112","doi":"10.1109/tbme.2006.876619","title":"Optimal reduction of MCG in fetal MEG recordings","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Welichem Biotech (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke","keywords":"Magnetoencephalography; Magnetocardiography; In utero; SIGNAL (programming language); Signal-to-noise ratio (imaging); Projection (relational algebra); Fetus; Noise reduction; Biomedical engineering; Speech recognition; Medicine; Electroencephalography; Computer science; Neuroscience; Artificial intelligence; Pregnancy; Biology; Internal medicine; Algorithm; Telecommunications","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":[0.0002235388,0.0001021321,0.0001370578,0.0004708815,0.00002569422,0.00002160179,0.0002186624,0.0001053614,0.000009452745],"category_scores_gemma":[0.000005237349,0.0001070742,0.0000645641,0.0007109997,0.00003711719,0.0002623835,0.000001961968,0.0002400722,0.000005024423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000629814,"about_ca_system_score_gemma":0.00002881272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009734203,"about_ca_topic_score_gemma":0.00000357687,"domain_scores_codex":[0.9990724,0.00001882424,0.0002966566,0.000205448,0.0002418364,0.0001648609],"domain_scores_gemma":[0.9996486,0.00003929887,0.00005165393,0.0001822211,0.00002524785,0.00005297997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003017631,0.0008614976,0.000006949634,0.00006334911,0.00003269042,0.00001918588,0.0007087716,0.6284285,0.244364,0.010534,0.0003954798,0.1145553],"study_design_scores_gemma":[0.0004707785,0.0002057535,0.000234293,0.00009100925,0.000005594119,0.00004564704,0.00001923029,0.6606798,0.3362827,0.0001767822,0.001550771,0.000237639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05475463,0.00001009627,0.9439367,0.0004213207,0.0003810506,0.00008506375,0.000002396383,0.0002937853,0.0001149426],"genre_scores_gemma":[0.9014395,0.000008259653,0.0984375,0.00001284032,0.00003084159,0.00001983942,0.000001130308,0.000008887429,0.00004121745],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8466849,"threshold_uncertainty_score":0.4366358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007006886011178489,"score_gpt":0.2188628949935221,"score_spread":0.2118560089823436,"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."}}