{"id":"W2970781318","doi":"10.32470/ccn.2019.1384-0","title":"Functional Decoding using Convolutional Networks on Brain Graphs","year":2019,"lang":"en","type":"article","venue":"2019 Conference on Cognitive Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Decoding methods; Computer science; Convolutional code; Convolutional neural network; Sequential decoding; Artificial intelligence; Algorithm","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004149595,0.0004333941,0.0003315494,0.0004490622,0.0008638853,0.000227766,0.0003988316,0.00009949365,0.0004171297],"category_scores_gemma":[0.008065801,0.0004338197,0.0001615692,0.0009740001,0.0007303684,0.000570897,0.0002067222,0.0005649725,0.0008934769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001422392,"about_ca_system_score_gemma":0.0003929893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007358691,"about_ca_topic_score_gemma":0.000001915674,"domain_scores_codex":[0.9954745,0.000382046,0.0003855386,0.001655361,0.001464322,0.0006382357],"domain_scores_gemma":[0.9817174,0.0170936,0.0002693879,0.0002272292,0.0005071536,0.0001851943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006209926,0.0004525716,0.004657256,0.00001694829,0.000014001,0.0000344307,0.00005860008,0.4756393,0.05194836,0.4589323,0.00587786,0.001747423],"study_design_scores_gemma":[0.001619826,0.0008774934,0.07587209,0.0002318446,0.00001308247,0.0001031374,0.00006447866,0.8967999,0.002897167,0.02039386,0.0004107899,0.0007163491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8500144,0.00002401493,0.1234952,0.007681821,0.005151318,0.001220408,0.0002855525,0.0003046529,0.01182261],"genre_scores_gemma":[0.9581611,0.00001135479,0.000170601,0.04050655,0.0001375782,0.00003013627,0.00002768835,0.00003235573,0.0009226347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4385384,"threshold_uncertainty_score":0.9998844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1067551455577668,"score_gpt":0.3105676049136964,"score_spread":0.2038124593559296,"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."}}