{"id":"W2023983729","doi":"10.4103/0028-3886.82714","title":"Non-normalized individual analysis of statistical parametric mapping for clinical fMRI","year":2011,"lang":"en","type":"article","venue":"Neurology India","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistical parametric mapping; Spatial normalization; Functional magnetic resonance imaging; Medicine; Normalization (sociology); Brain mapping; Statistical analysis; Magnetic resonance imaging; Parametric statistics; Artificial intelligence; Functional imaging; Pattern recognition (psychology); Neuroscience; Radiology; Computer science; Psychology; Statistics","routes":{"ca_aff":false,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008490512,0.0001624055,0.0006302478,0.0008503083,0.0001407581,0.000009638787,0.0003379644,0.0001874116,0.0001547028],"category_scores_gemma":[0.01900656,0.0001553534,0.0002559585,0.001616529,0.0005192597,0.00008369173,0.0001727522,0.0003344177,0.00002576036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008197969,"about_ca_system_score_gemma":0.00005731246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002559687,"about_ca_topic_score_gemma":0.00001478356,"domain_scores_codex":[0.997732,0.0003659043,0.0005949449,0.0006636741,0.0002664669,0.000376953],"domain_scores_gemma":[0.9778088,0.02143911,0.0002795822,0.0003098981,0.00007445778,0.00008816001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009788041,0.0006018382,0.9802581,0.00002722267,0.0009171139,0.00007019827,0.0008260111,0.0000401624,0.0008778734,0.007808989,0.005428301,0.002165382],"study_design_scores_gemma":[0.0008730537,0.001051728,0.9917117,0.000001025302,0.0005420072,0.000009177656,0.00001664953,0.001477711,0.001454992,0.001170953,0.001555183,0.0001357869],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909025,0.00001409583,0.006069594,0.0004046135,0.0007092689,0.0003578168,0.000278421,0.00004810706,0.001215588],"genre_scores_gemma":[0.9919483,0.00002306673,0.001562705,0.006288371,0.00006107448,0.00006928945,0.00001340157,0.00001510773,0.00001868924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02107321,"threshold_uncertainty_score":0.9892567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1667562301701907,"score_gpt":0.3601720313221325,"score_spread":0.1934158011519418,"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."}}