{"id":"W2021377337","doi":"10.1016/s1053-8119(03)00362-8","title":"Functional magnetic resonance imaging at 0.2 Tesla","year":2003,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Functional magnetic resonance imaging; Nuclear magnetic resonance; Blood oxygenation; Functional magnetic resonance spectroscopy of the brain; Magnetic resonance imaging; SIGNAL (programming language); Physics; Neuroscience; Psychology; Computer science; Medicine; Radiology","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":[0.00004496102,0.000087945,0.00009190221,0.00003452382,0.000100091,0.000007407263,0.00003612084,0.00001950351,0.0008504135],"category_scores_gemma":[0.00006793852,0.00008340595,0.00004530205,0.0001390137,0.00006192133,0.00004460932,0.00002420229,0.0001237069,0.0001796959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003781921,"about_ca_system_score_gemma":0.00002192578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000143326,"about_ca_topic_score_gemma":4.032373e-7,"domain_scores_codex":[0.9993358,0.00001299333,0.0001152723,0.0002420767,0.0001261114,0.0001677238],"domain_scores_gemma":[0.9995239,0.00003274369,0.00002564038,0.0003010969,0.00004365763,0.00007290767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000112975,0.0003751856,0.03977741,0.00003484675,0.000001803765,0.0002963376,0.00003849903,0.00002060779,0.5238989,0.03347348,0.295728,0.1062419],"study_design_scores_gemma":[0.00047515,0.0000586284,0.08017587,0.00001324802,0.00001431934,0.0003986266,0.000006556483,0.0001757666,0.01829321,0.0009843807,0.8993082,0.000096013],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09546297,0.01362837,0.1204233,0.01103336,0.0004338043,0.001975941,0.0000573336,0.001720876,0.755264],"genre_scores_gemma":[0.7627584,0.0003606785,0.09169402,0.009191021,0.0002290368,0.0002401463,0.00004275627,0.0001090097,0.1353749],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6672955,"threshold_uncertainty_score":0.9311431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02071257745133569,"score_gpt":0.2810434545239622,"score_spread":0.2603308770726265,"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."}}