{"id":"W1994454049","doi":"10.1006/nimg.2002.1096","title":"Estimating the Delay of the fMRI Response","year":2002,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":155,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Voxel; Computer science; Artificial intelligence; Blood-oxygen-level dependent; Stimulus (psychology); Perception; Pattern recognition (psychology); Inference; Set (abstract data type); Computer vision; Functional magnetic resonance imaging; Psychology; Cognitive 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003288179,0.0000903711,0.00008647439,0.0000256065,0.000414833,0.00002699527,0.0003850614,0.00001673184,0.00007777883],"category_scores_gemma":[0.03052429,0.00004989234,0.00007602771,0.000338211,0.0003180561,0.00009282564,0.0002192366,0.0001996144,0.00007740946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001337479,"about_ca_system_score_gemma":0.000009935509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005116751,"about_ca_topic_score_gemma":0.000002586868,"domain_scores_codex":[0.9985985,0.0005372257,0.0001377559,0.0002573502,0.0003103032,0.0001588283],"domain_scores_gemma":[0.9897458,0.009629993,0.00009121595,0.0004878005,0.00002808855,0.00001711225],"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":[0.000112066,0.00009132568,0.001108054,0.00001272913,0.000004735383,0.00003171869,0.0009017296,0.001483974,0.9551355,0.0007887101,0.03800394,0.002325503],"study_design_scores_gemma":[0.001178947,0.0005051083,0.2118818,0.00007007274,0.00005333465,0.0005373348,0.0001369936,0.1730461,0.5345083,0.002464608,0.07510408,0.0005133097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568277,0.00003056441,0.000125495,0.0352697,0.0006700824,0.000190861,0.000009378817,0.00005448582,0.006821755],"genre_scores_gemma":[0.991509,0.000002023353,0.0001318485,0.006626188,0.00005701793,0.0000107555,1.9024e-8,0.00001245443,0.001650705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4206272,"threshold_uncertainty_score":0.977642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05974915194634867,"score_gpt":0.2693840971186019,"score_spread":0.2096349451722532,"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."}}