{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003190296,0.0004751266,0.0002078542,0.001176173,0.0001526814,0.0007210352,0.0002622173,0.0003792638,0.00238881],"category_scores_gemma":[0.001963023,0.0002046675,0.0002934498,0.001298117,0.000275717,0.0008381755,0.000511498,0.000502218,0.0007543022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002357,"about_ca_system_score_gemma":0.0003595307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003564177,"about_ca_topic_score_gemma":0.0007295822,"domain_scores_codex":[0.9998951,0.00002134986,0.000007588225,0.00002179141,0.00003739147,0.00001674757],"domain_scores_gemma":[0.9997736,0.00007038539,0.00004804084,0.00003753411,0.0000499864,0.00002052829],"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":[0.0004922142,0.00007457527,0.004970171,0.000485232,0.000136334,0.000564525,0.0002227213,0.01096198,0.6173076,0.009334582,0.002953832,0.3524962],"study_design_scores_gemma":[0.000158994,0.0007222366,0.1182901,0.0001909178,0.0004682314,0.006052192,0.0004805518,0.3935196,0.4067312,0.05667483,0.01653284,0.0001783402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2080624,0.001168065,0.7800984,0.0004380612,0.00009739814,0.0001389857,0.001970598,0.001154684,0.006871487],"genre_scores_gemma":[0.706467,0.002442602,0.2854083,0.0001808857,0.0003026262,0.0001441418,0.002715777,0.0003515444,0.001987212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00238881,"threshold_uncertainty_score":0.007991374,"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."}}