{"id":"W2164358613","doi":"10.1109/tmi.2008.929097","title":"Spatial Characterization of fMRI Activation Maps Using Invariant 3-D Moment Descriptors","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Voxel; Pattern recognition (psychology); Artificial intelligence; Computer science; Functional magnetic resonance imaging; Region of interest; Invariant (physics); Contrast (vision); Spatial analysis; Computer vision; Mathematics; Statistics; Psychology; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005221509,0.0004854493,0.0005466767,0.002236248,0.0001383178,0.0008998733,0.000575061,0.0004028975,0.0005650769],"category_scores_gemma":[0.001531245,0.0002003097,0.0008072678,0.001344955,0.0004962753,0.0007610027,0.0005165972,0.0004176246,0.0002666016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002953207,"about_ca_system_score_gemma":0.0005089479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007824685,"about_ca_topic_score_gemma":0.0008120694,"domain_scores_codex":[0.9997345,0.00005148552,0.0000237178,0.0000678233,0.00008546452,0.00003699917],"domain_scores_gemma":[0.9993483,0.000198415,0.0001924829,0.0001037249,0.000117222,0.0000397655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000342717,0.0001658035,0.008574145,0.0003129244,0.0001899919,0.0003156895,0.0002052913,0.08663966,0.3880799,0.01548876,0.001682154,0.498003],"study_design_scores_gemma":[0.00002911106,0.0002935862,0.0312512,0.00002698276,0.00009040182,0.0009207032,0.0001059756,0.875074,0.076248,0.01202681,0.003777598,0.0001556359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03867205,0.0001618873,0.9601517,0.00004918916,0.00001640443,0.00003515035,0.0001770113,0.0003629027,0.000373626],"genre_scores_gemma":[0.5360413,0.0003989415,0.4618403,0.00006808662,0.00007868535,0.000148775,0.0006477424,0.0001735022,0.0006027442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002236248,"threshold_uncertainty_score":0.002761424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04651870957083615,"score_gpt":0.2617588303170818,"score_spread":0.2152401207462457,"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."}}