{"id":"W1516958410","doi":"10.1109/iembs.2006.260420","title":"A Combined SPM-ICA Approach to fMRI","year":2006,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Independent component analysis; Statistical parametric mapping; Artificial intelligence; Computer science; Voxel; Pattern recognition (psychology); Haemodynamic response; Functional magnetic resonance imaging; Neuroimaging; Parametric statistics; Psychology; Neuroscience; Mathematics; Magnetic resonance imaging","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001932286,0.00007642969,0.00008619913,0.00009677563,0.00004795553,0.0001597459,0.0005606548,0.00003892531,0.00001223638],"category_scores_gemma":[0.000007486883,0.00006612953,0.00003193593,0.0003782079,0.00001168969,0.0002049844,0.0001553144,0.0000594204,0.0001493637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001643639,"about_ca_system_score_gemma":0.0000242389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000951221,"about_ca_topic_score_gemma":0.000006161584,"domain_scores_codex":[0.9992531,0.0000352811,0.00013771,0.0002459711,0.0001767027,0.0001512362],"domain_scores_gemma":[0.9994305,0.00002008137,0.00002393353,0.0004252431,0.00004525547,0.00005495797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001283719,0.00009994736,0.00006752056,0.000001300863,0.000001238392,5.335107e-7,0.0001453573,0.00007675964,0.0003915258,0.9435897,0.05440978,0.001215021],"study_design_scores_gemma":[0.00159037,0.0007589862,0.02252074,0.00001989429,0.000008877619,0.0000351609,0.00009329067,0.2606065,0.1447155,0.228886,0.3391553,0.001609429],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001800151,0.000002370144,0.6768785,0.001926048,0.00002247585,0.0001366451,1.586614e-7,0.0006507679,0.3185829],"genre_scores_gemma":[0.5189715,1.902035e-7,0.4741279,0.001975451,0.00002095208,0.00002646379,0.000002017684,0.000004211305,0.004871246],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7147037,"threshold_uncertainty_score":0.2696684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233608100782324,"score_gpt":0.2340044592688285,"score_spread":0.2216683782610052,"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."}}