{"id":"W6930274268","doi":"10.5281/zenodo.1328279","title":"mrivis: neuroimaging visualization library and development toolkit","year":2018,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Visualization; Neuroimaging; Information visualization; Data visualization; Development (topology)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008419331,0.0001967072,0.0001489089,0.0004300111,0.0004467187,0.0005082797,0.0004126281,0.0001018714,0.01553077],"category_scores_gemma":[0.00002713804,0.0002021848,0.00002465595,0.0005130703,0.0000914476,0.0002066964,0.0004020187,0.0001614325,0.003586933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002099019,"about_ca_system_score_gemma":0.000002110311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.72591e-7,"about_ca_topic_score_gemma":5.928241e-8,"domain_scores_codex":[0.9989616,0.00007165009,0.0001729369,0.000312805,0.0001915206,0.0002894492],"domain_scores_gemma":[0.9995736,0.000007084197,0.00004990006,0.000206755,0.00003762685,0.0001250585],"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":[0.000004734675,0.00002070202,0.000005895109,0.0001617198,0.00003634048,0.000005471299,0.0001112009,0.000003017441,0.0002967012,0.0004253516,0.884766,0.1141628],"study_design_scores_gemma":[0.0001418246,0.00005123875,0.0001367364,0.00008826374,0.000008299866,0.00002260905,0.00001203978,0.0005198669,0.0003263692,0.00001959999,0.9984323,0.0002408992],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0003099866,0.0008363202,0.005347528,0.00006909675,0.0001444147,0.0003326991,0.00005820749,0.005563116,0.9873386],"genre_scores_gemma":[0.04912513,0.01722612,0.01168944,0.001454854,0.00522604,8.231953e-7,0.02207116,0.112249,0.7809574],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2063812,"threshold_uncertainty_score":0.9971889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574741088393903,"score_gpt":0.2073822381318635,"score_spread":0.1916348272479245,"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."}}