{"id":"W2100851166","doi":"10.3390/s150924409","title":"Toward Epileptic Brain Region Detection Based on Magnetic Nanoparticle Patterning","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Epilepsy; Magnetic resonance imaging; Neuroscience; Superparamagnetism; Computer science; Electroencephalography; Epileptic seizure; Medicine; Radiology; Magnetic field; Psychology; Physics; Magnetization","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":[0.00008189183,0.0001766432,0.0001619051,0.0001305981,0.0001289749,0.0002415718,0.0002339459,0.0004026188,0.0005246261],"category_scores_gemma":[0.0003098507,0.0001803255,0.0001873244,0.00009455679,0.0002636125,0.0003537189,0.0002548829,0.0001442235,0.0001996652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002583587,"about_ca_system_score_gemma":0.0001525983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006272942,"about_ca_topic_score_gemma":0.000641509,"domain_scores_codex":[0.999941,0.00001042528,0.000002445122,0.00001568462,0.00002412918,0.000006346643],"domain_scores_gemma":[0.9999312,0.00003019909,0.00001625499,0.000007225992,0.00001117023,0.00000393171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004497844,0.00003745099,0.000571863,0.0001220644,0.00001048772,0.0003014599,0.00008579077,0.07802558,0.8971877,0.01016459,0.0005419776,0.0129061],"study_design_scores_gemma":[0.00001822842,0.00008873836,0.0006104246,0.00001059267,0.00001012695,0.0002160582,0.00002468682,0.7579505,0.2356584,0.002370288,0.003024293,0.00001776074],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4789801,0.0009975837,0.4979327,0.0007500281,0.0001242253,0.0001295699,0.0001163891,0.0007578322,0.02021147],"genre_scores_gemma":[0.9035654,0.0005105672,0.09212987,0.00008619684,0.00001292164,0.00007664423,0.00003859843,0.00003133795,0.003548485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006272942,"threshold_uncertainty_score":0.001874506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02596922234993372,"score_gpt":0.2119346167487711,"score_spread":0.1859653943988374,"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."}}