{"id":"W1584031041","doi":"10.1007/978-3-540-85988-8_121","title":"Discovering Structure in the Space of Activation Profiles in fMRI","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; National Science Foundation","keywords":"Computer science; Context (archaeology); Artificial intelligence; Space (punctuation); Functional magnetic resonance imaging; Visual cortex; Pattern recognition (psychology); Neuroscience; Psychology","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":[0.001276236,0.0005417867,0.0006963547,0.003107825,0.0005242092,0.001583335,0.0006199636,0.0008419517,0.001327089],"category_scores_gemma":[0.006869908,0.0005030418,0.0008920022,0.001958435,0.0008231037,0.001641522,0.0007756918,0.001399124,0.0004921397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002652644,"about_ca_system_score_gemma":0.0007260688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001853343,"about_ca_topic_score_gemma":0.002602148,"domain_scores_codex":[0.9995765,0.0001690632,0.00002621969,0.000106856,0.00005913207,0.00006223201],"domain_scores_gemma":[0.9967638,0.002367167,0.000305801,0.0002493453,0.000176654,0.0001370836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0019752,0.0007639923,0.04945964,0.0006492575,0.0004420725,0.001392886,0.001219989,0.1251726,0.1404664,0.03939267,0.009778076,0.6292872],"study_design_scores_gemma":[0.00005305636,0.0002669726,0.03539996,0.00008298821,0.00009521653,0.0009471693,0.0004527139,0.8048989,0.01089721,0.1449229,0.001908692,0.00007424699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3940562,0.0009895812,0.6008085,0.0009564824,0.00003084602,0.00005942427,0.001217668,0.0007809352,0.001100297],"genre_scores_gemma":[0.8924959,0.0008696529,0.1037197,0.0001126539,0.0001326539,0.00007302783,0.001597936,0.0001571697,0.0008413473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003107825,"threshold_uncertainty_score":0.006749511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0280925165832064,"score_gpt":0.2567891094032594,"score_spread":0.2286965928200531,"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."}}