{"id":"W2089400065","doi":"10.1016/j.bspc.2007.09.004","title":"Investigation of a two-point maximum entropy regularization method for signal enhancement applied to magnetoencephalography data","year":2007,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Down Syndrome Research Foundation; University of Victoria","funders":"Down Syndrome Research Foundation","keywords":"Magnetoencephalography; Computer science; Filter (signal processing); Entropy (arrow of time); Noise reduction; Algorithm; Noise (video); SIGNAL (programming language); Imaging phantom; Artificial intelligence; Mathematics; Pattern recognition (psychology); Computer vision; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002585672,0.0001380264,0.0002293026,0.000246864,0.0001208456,0.0001200991,0.0005417104,0.00008971622,0.000007497933],"category_scores_gemma":[0.00003756722,0.0001194902,0.00003107764,0.0005585772,0.0001280117,0.0002874373,0.0001243797,0.00008820099,7.337156e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001836956,"about_ca_system_score_gemma":0.0001485423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001169045,"about_ca_topic_score_gemma":0.000001866423,"domain_scores_codex":[0.9982594,0.000077684,0.0004686334,0.0004912156,0.0004444332,0.000258671],"domain_scores_gemma":[0.998939,0.0001957986,0.000223268,0.0002621485,0.000161119,0.0002187017],"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":[0.0001125247,0.00005512414,0.00003537186,0.00006623723,0.00001752343,7.940079e-7,0.0005139401,0.000007037448,0.4474977,0.01462454,0.0002553604,0.5368139],"study_design_scores_gemma":[0.003903688,0.001271305,0.0002800577,0.0001709021,0.00008166234,0.000009027285,0.0001000635,0.6036516,0.2763496,0.1092538,0.004457214,0.0004710574],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007355934,0.000142757,0.9964095,0.001882532,0.00002722149,0.0006147383,0.00001135892,0.0001085938,0.00006771606],"genre_scores_gemma":[0.5707795,0.000001551353,0.4277899,0.001270537,0.00006166333,0.00004381478,0.00004116474,0.000005262213,0.000006597335],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6036445,"threshold_uncertainty_score":0.4872671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225993278268345,"score_gpt":0.2982642080964655,"score_spread":0.275664880269631,"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."}}