{"id":"W2103513921","doi":"10.1007/s10548-012-0217-2","title":"Techniques for Detection and Localization of Weak Hippocampal and Medial Frontal Sources Using Beamformers in MEG","year":2012,"lang":"en","type":"article","venue":"Brain Topography","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; University of Toronto; Baycrest Hospital; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Magnetoencephalography; Subtraction; Computer science; Artificial intelligence; Pattern recognition (psychology); Hippocampal formation; Elementary cognitive task; Speech recognition; Cognition; Neuroscience; Electroencephalography; Psychology; Mathematics","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":[0.00123519,0.0008018104,0.0004649223,0.0009984087,0.0003523143,0.000842703,0.0008360887,0.0008895539,0.00343442],"category_scores_gemma":[0.003728118,0.0008184836,0.0005919532,0.001104671,0.0006927355,0.001401662,0.0009071158,0.001508789,0.00166559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001863126,"about_ca_system_score_gemma":0.0004182998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008102576,"about_ca_topic_score_gemma":0.002317219,"domain_scores_codex":[0.999736,0.00008216641,0.00002087522,0.00004718587,0.0000889885,0.00002475138],"domain_scores_gemma":[0.9988893,0.0006943673,0.00008194741,0.0001457382,0.0001590652,0.00002957778],"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":[0.0002569741,0.00006690984,0.001782722,0.0003972211,0.00009969489,0.0001445865,0.0002526416,0.0050475,0.6005287,0.007654932,0.002162099,0.381606],"study_design_scores_gemma":[0.0003722783,0.0005811517,0.03875865,0.0002710134,0.0004736308,0.004434589,0.0004762851,0.2117229,0.6462802,0.06459078,0.03175364,0.0002848915],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005127766,0.0004443515,0.9932721,0.0001483175,0.00002806557,0.00004239088,0.0001099322,0.0003309192,0.000496279],"genre_scores_gemma":[0.06973905,0.00139521,0.9271729,0.0001254292,0.00008333266,0.0002025711,0.0001621181,0.000135685,0.0009837626],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00343442,"threshold_uncertainty_score":0.01148927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903445951139229,"score_gpt":0.2554903495839094,"score_spread":0.2364558900725171,"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."}}