{"id":"W29531279","doi":"10.1038/s41598-018-21603-7","title":"Activation Detection and Characterisation in Brain fMRI Sequences. Application to the study of monkey vision.","year":2001,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Artificial intelligence; Smoothing; Pattern recognition (psychology); Autocorrelation; Impulse response; Cluster analysis; Impulse (physics); Statistical power; Algorithm; Machine learning; Computer vision; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000587,0.00006066629,0.00007154777,0.0001750752,0.00005797778,0.00006812804,0.0002060211,0.0000351161,0.000002059111],"category_scores_gemma":[0.00004068561,0.00004575096,0.000008449014,0.0006560108,0.000009794932,0.000601602,0.0000656369,0.00006608227,0.000003591422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000351827,"about_ca_system_score_gemma":0.00001159606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005896564,"about_ca_topic_score_gemma":0.0008940208,"domain_scores_codex":[0.9992039,0.0001247031,0.000210042,0.0002151856,0.0001774541,0.00006877154],"domain_scores_gemma":[0.9994615,0.00007284465,0.00009832469,0.000281518,0.00006446509,0.0000213069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003567155,0.0004003961,0.009515458,0.000005628484,0.000006336792,4.42418e-7,0.02029944,0.001072233,0.2187046,0.006420065,0.0001077009,0.743432],"study_design_scores_gemma":[0.0004396625,0.0006934285,0.7710397,0.00001421493,0.000002239821,0.000005530274,0.001209388,0.1512645,0.07057787,0.002642955,0.00193946,0.0001709898],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5124183,9.08913e-7,0.4840108,0.002920002,0.000009560264,0.0004686727,5.73999e-8,0.00005285552,0.0001188131],"genre_scores_gemma":[0.9972188,0.000004090545,0.00204661,0.0005497725,0.00001252544,0.000128036,8.921218e-7,0.000003064016,0.00003614031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7615243,"threshold_uncertainty_score":0.186567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476251658886469,"score_gpt":0.2889567657326153,"score_spread":0.2741942491437506,"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."}}