{"id":"W2888488583","doi":"10.1016/j.neuroimage.2018.08.031","title":"Phase shift invariant imaging of coherent sources (PSIICOS) from MEG data","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Health and Safety Executive","keywords":"Magnetoencephalography; Coherence (philosophical gambling strategy); Lag; Computer science; Phase lag; Amplitude; Invariant (physics); Mutual coherence; Pattern recognition (psychology); Phase (matter); Physics; Artificial intelligence; Electroencephalography; Algorithm; Statistical physics; Biological system; Mathematics; Optics; Statistics; Mathematical analysis; Quantum mechanics","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":[0.0001514709,0.000164248,0.0001872098,0.00007540613,0.0001493625,0.0001114795,0.0008111263,0.0000274293,0.0003022924],"category_scores_gemma":[0.0005043421,0.0001437039,0.00004475502,0.0002189437,0.0002874514,0.0004582929,0.0005296186,0.0001697418,0.000115185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009678408,"about_ca_system_score_gemma":0.00002916888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002168559,"about_ca_topic_score_gemma":0.00004209892,"domain_scores_codex":[0.9982919,0.0001497486,0.0002904738,0.0007348743,0.0002761416,0.0002569139],"domain_scores_gemma":[0.9983914,0.0002736548,0.0001806165,0.001034373,0.00003204108,0.00008786805],"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":[0.00007880246,0.0002728628,0.0005361027,0.00000728313,0.00000267872,0.00005730148,0.00009896979,0.00000128651,0.9904628,0.0004828499,0.001852259,0.006146741],"study_design_scores_gemma":[0.003588213,0.0008117339,0.01493257,0.00005911909,0.00007420617,0.0000393688,0.00004531502,0.1442543,0.8068787,0.003640245,0.0251503,0.0005259313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993364,0.00001802124,0.001828468,0.0008833321,0.0008433374,0.0001939007,0.0005776744,0.00009353229,0.00219774],"genre_scores_gemma":[0.9976223,0.00001347299,0.000155783,0.001758106,0.0002720763,0.000003037955,0.00005352824,0.00002791615,0.00009383816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1835841,"threshold_uncertainty_score":0.5860076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07285081879302983,"score_gpt":0.3132534742558319,"score_spread":0.240402655462802,"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."}}