{"id":"W6958414580","doi":"10.6084/m9.figshare.17073459.v1","title":"Additional file 1 of Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Genetic and Environmental Crop Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Relevance (law); Visualization; Artificial neural network; Table (database); Data visualization; Encoding (memory)","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":[],"category_scores_codex":[0.00002533759,0.00006438583,0.00009821955,0.000003857285,0.00004742396,0.00000439829,0.00004820517,0.00002616044,0.8911445],"category_scores_gemma":[0.0007684199,0.00003537147,0.00004417278,0.0001324112,0.00002039127,0.00006623501,0.00009333518,0.00003826962,0.00004019753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002646701,"about_ca_system_score_gemma":0.0000185328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001342379,"about_ca_topic_score_gemma":0.0001425595,"domain_scores_codex":[0.9992758,0.00008572787,0.0001757646,0.0001710731,0.0002002175,0.00009141097],"domain_scores_gemma":[0.9989699,0.000703261,0.0001301794,0.00003302747,0.0001356061,0.00002808823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00005658574,0.0001685342,0.001738596,0.00003480395,0.00002121011,0.000001958622,0.00003361346,0.002800527,0.001568461,0.000001923386,0.9499132,0.04366057],"study_design_scores_gemma":[0.00007508827,0.00004153034,0.968376,0.0003473171,0.00001083305,7.730875e-7,0.00007613047,0.01522663,0.0001453975,0.000141431,0.01548273,0.00007613936],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01830759,0.0005039832,3.417136e-7,0.0000224008,0.00001876444,0.0001423256,0.9808466,0.000005271088,0.0001527228],"genre_scores_gemma":[0.466242,0.000001165312,0.0000741119,0.00002476494,0.00003817887,0.0001045948,0.5335045,3.928535e-7,0.00001030696],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9666374,"threshold_uncertainty_score":0.1442407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05162753134107539,"score_gpt":0.2580851328234828,"score_spread":0.2064576014824074,"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."}}