{"id":"W2900083938","doi":"10.3389/fninf.2018.00077","title":"The CAMH Neuroinformatics Platform: A Hospital-Focused Brain-CODE Implementation","year":2018,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Indoc Research; University of Toronto; Public Health Ontario; Baycrest Hospital; Ontario Brain Institute; Centre for Addiction and Mental Health","funders":"Canada Foundation for Innovation; Government of Ontario","keywords":"Neuroinformatics; Standardization; Computer science; Context (archaeology); Data science; Analytics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005969285,0.0003429576,0.0003073928,0.0002830617,0.001093124,0.0003250362,0.0007424963,0.00007940645,0.00001410418],"category_scores_gemma":[0.007319116,0.0002717536,0.0001130346,0.0008653801,0.0005960469,0.001555605,0.0003329225,0.0004293405,0.0001509934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001767451,"about_ca_system_score_gemma":0.0001341136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001122173,"about_ca_topic_score_gemma":0.0001040124,"domain_scores_codex":[0.9970688,0.00009974223,0.000964053,0.0003111661,0.0007583255,0.0007978747],"domain_scores_gemma":[0.9958791,0.002750689,0.0004426097,0.0006988353,0.0001125437,0.0001162766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001753754,0.000108887,0.006339139,0.00008798656,0.00003462714,0.00002276811,0.01297405,0.0002668094,0.0004225013,0.006448466,0.9174701,0.05564929],"study_design_scores_gemma":[0.004301264,0.002258921,0.01358353,0.00006476272,0.00004499329,0.00008996496,0.01300377,0.2121896,0.02462445,0.01148562,0.7170536,0.001299533],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8871931,0.000103416,0.02738655,0.04139837,0.01741802,0.003652932,0.0002050094,0.0007110994,0.02193153],"genre_scores_gemma":[0.9574146,0.0003754402,0.009862526,0.03037185,0.0005619134,0.0001864831,0.00002100208,0.0001236501,0.00108251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2119228,"threshold_uncertainty_score":0.9999735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0237977584798663,"score_gpt":0.2733470155481162,"score_spread":0.2495492570682499,"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."}}