{"id":"W4315701213","doi":"10.22489/cinc.2022.419","title":"Segmentation Uncertainty Quantification in Cardiac Propagation Models","year":2022,"lang":"en","type":"article","venue":"Computing in cardiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Dalhousie University","keywords":"Segmentation; Pipeline transport; Torso; Computer science; Pipeline (software); Eikonal equation; Computation; Image segmentation; Algorithm; Artificial intelligence; Mathematics; Engineering; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001817915,0.00008517053,0.0003121042,0.0001333198,0.00008888484,0.00001005014,0.0001147282,0.00004494038,0.000009171302],"category_scores_gemma":[0.0004010935,0.00009040975,0.0000445466,0.0002954686,0.00003713792,0.00003533337,0.00009604741,0.0002399788,0.000001891597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002379219,"about_ca_system_score_gemma":0.00004548998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006719518,"about_ca_topic_score_gemma":0.000006395187,"domain_scores_codex":[0.9977782,0.00125848,0.0003634726,0.0002684576,0.0001359658,0.0001953762],"domain_scores_gemma":[0.9985135,0.001151014,0.000107099,0.0001726775,0.00003668191,0.0000189839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004011451,0.0000559266,0.02328213,0.00006828696,0.0000180279,0.0000195069,0.001379452,0.1900031,0.001149599,0.7043765,0.0002462437,0.07936103],"study_design_scores_gemma":[0.0002591228,0.0000502893,0.01199828,0.00001441312,0.000008358965,0.000007477324,0.0004921678,0.3916777,0.00006642402,0.59527,0.00003711042,0.0001186611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1478956,0.00003628148,0.8500931,0.00007829174,0.0003699411,0.000321348,0.0000131034,0.00003360165,0.001158712],"genre_scores_gemma":[0.8719363,0.000003726326,0.1278754,0.00002585734,0.00004931113,0.00006679467,0.00002869992,0.000008632342,0.000005310545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7240407,"threshold_uncertainty_score":0.3686802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1016723115243171,"score_gpt":0.3783310634152719,"score_spread":0.2766587518909548,"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."}}