{"id":"W4387211282","doi":"10.1007/978-3-031-43990-2_53","title":"Bridging Ex-Vivo Training and Intra-operative Deployment for Surgical Margin Assessment with Evidential Graph Transformer","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Queen's University","funders":"","keywords":"Interpretability; Computer science; Ex vivo; Software deployment; Artificial intelligence; Transformer; Machine learning; Data mining; In vivo; Voltage","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008482836,0.0004441523,0.00028917,0.0005193682,0.0001431561,0.0008514366,0.0009227335,0.0007057754,0.005380195],"category_scores_gemma":[0.002430065,0.0003060484,0.0002955325,0.0003393309,0.000463372,0.001180973,0.001167109,0.000695351,0.001911296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000213763,"about_ca_system_score_gemma":0.0003611191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003844538,"about_ca_topic_score_gemma":0.0007742196,"domain_scores_codex":[0.9996487,0.00008098728,0.00002270986,0.00008820614,0.0001328171,0.00002651226],"domain_scores_gemma":[0.9989095,0.0005851106,0.00006594219,0.0002454254,0.0001600119,0.00003401951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004792739,0.000130759,0.001396942,0.0003221471,0.0000291111,0.000228606,0.0002111047,0.02488315,0.3010575,0.007616036,0.00453035,0.659115],"study_design_scores_gemma":[0.00003389741,0.0005704423,0.00520158,0.0001277284,0.00007001063,0.002515712,0.0002592211,0.526464,0.4073189,0.02675055,0.03059835,0.00008968708],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02148448,0.0002440709,0.973444,0.000128664,0.00007004569,0.00005443408,0.0001638753,0.001396987,0.003013444],"genre_scores_gemma":[0.4466326,0.0006256074,0.5429544,0.0001646988,0.00005292389,0.00007909467,0.0005120154,0.0008234276,0.008155257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005380195,"threshold_uncertainty_score":0.01799852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349493775551887,"score_gpt":0.3119010433949428,"score_spread":0.2769516658397542,"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."}}