{"id":"W3210534354","doi":"10.3390/diagnostics11112025","title":"Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography","year":2021,"lang":"en","type":"article","venue":"Diagnostics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Segmentation; Coronavirus disease 2019 (COVID-19); Computed tomography; Ground truth; Artificial intelligence; Deep learning; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computer science; Pattern recognition (psychology); Medicine; Radiology; Pathology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000851778,0.0002158245,0.0005701456,0.0003136767,0.00009195829,0.00004250522,0.0001219832,0.00008427289,0.00003389112],"category_scores_gemma":[0.01119625,0.0002435116,0.0001410093,0.0007041951,0.00006955752,0.0001366161,0.0001309203,0.0002916586,0.000001204442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004287191,"about_ca_system_score_gemma":0.0003750301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000346464,"about_ca_topic_score_gemma":0.0003085175,"domain_scores_codex":[0.9977434,0.0003704799,0.0006912808,0.000554075,0.0003434462,0.0002972875],"domain_scores_gemma":[0.9911051,0.007557608,0.0002137117,0.0004427572,0.0004686966,0.0002121678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002358417,0.005142359,0.8405354,0.001237961,0.0002040681,0.000245358,0.005898016,0.1405431,0.0002826481,0.0002305181,0.00223578,0.003208942],"study_design_scores_gemma":[0.02186405,0.002680139,0.158603,0.0009990679,0.001323545,0.00004361388,0.007238274,0.7918355,0.008512968,0.003371408,0.002711241,0.0008171429],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7163239,0.0002991069,0.2792889,0.001921264,0.0002539848,0.001753269,0.00004440757,0.00009602538,0.0000191462],"genre_scores_gemma":[0.990231,0.0001031076,0.004795921,0.004200913,0.00006516122,0.000229155,0.0003242171,0.00003835793,0.00001213678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6819324,"threshold_uncertainty_score":0.9971329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04568261746017982,"score_gpt":0.3480724746595885,"score_spread":0.3023898571994087,"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."}}