{"id":"W2143584197","doi":"10.1109/tbme.2003.820999","title":"Biomagnetic Source Detection by Maximum Entropy and Graphical Models","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université de Montréal","funders":"Centre de Recherches Mathématiques","keywords":"Magnetoencephalography; Entropy (arrow of time); Principle of maximum entropy; Probabilistic logic; Random variable; Mutual information; Computer science; Inverse problem; Regularization (linguistics); Markov random field; Measure (data warehouse); Markov process; Artificial intelligence; Algorithm; Pattern recognition (psychology); Mathematics; Data mining; Statistics; Electroencephalography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002397229,0.0009614794,0.0014424,0.002715211,0.0004542542,0.001506619,0.001410466,0.001596197,0.001350058],"category_scores_gemma":[0.01286666,0.0008644724,0.001409405,0.001466462,0.00173112,0.002948659,0.002143939,0.001352001,0.0003989175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059251,"about_ca_system_score_gemma":0.0005006787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001538948,"about_ca_topic_score_gemma":0.00132843,"domain_scores_codex":[0.9982357,0.00106383,0.0000642774,0.0002619609,0.0002916728,0.00008259452],"domain_scores_gemma":[0.9921199,0.006661263,0.0005725978,0.0003640518,0.0001812473,0.0001010878],"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":[0.0001469971,0.00004934799,0.001169859,0.0001552723,0.0001580627,0.0001611274,0.0001708997,0.7240728,0.002987929,0.1870552,0.001394619,0.08247781],"study_design_scores_gemma":[0.00000785749,0.00001085206,0.0001392232,0.000008810031,0.000007881701,0.00003081708,0.000003444147,0.8882269,0.0003513856,0.110866,0.0003330536,0.00001365989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005211038,0.0002226589,0.9937244,0.0002166388,0.000009713218,0.000009166301,0.00005326484,0.0001633832,0.0003896856],"genre_scores_gemma":[0.5972949,0.001095833,0.397611,0.0002884391,0.0002580465,0.0001892007,0.0004867984,0.0001950562,0.00258079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002715211,"threshold_uncertainty_score":0.01267791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00815340251252965,"score_gpt":0.190108317349442,"score_spread":0.1819549148369124,"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."}}