{"id":"W6948899257","doi":"10.5061/dryad.9s4mw6mc9","title":"Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tubulopathy; Nucleofection; Hemopericardium; Main Pulmonary Artery; Subpoena; Liquation","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":[0.001099158,0.001654344,0.0009391078,0.001732027,0.0003356367,0.001305495,0.001485333,0.001593188,0.003815878],"category_scores_gemma":[0.003378629,0.0004826408,0.001302909,0.0008185928,0.0004024499,0.0008227795,0.001217507,0.0009706541,0.002321456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000839114,"about_ca_system_score_gemma":0.001256871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004783347,"about_ca_topic_score_gemma":0.00689047,"domain_scores_codex":[0.9993529,0.0001122724,0.00007510991,0.0002114089,0.0001712846,0.00007705404],"domain_scores_gemma":[0.9991022,0.0002708847,0.0001352354,0.0001620729,0.0002755186,0.00005414552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002341961,0.001072801,0.03543486,0.002034192,0.001083641,0.001279604,0.0002228427,0.1143257,0.03180752,0.002302198,0.1349966,0.673098],"study_design_scores_gemma":[0.0003944242,0.0007464454,0.02811758,0.000429536,0.0003484415,0.002054534,0.0001413619,0.8515962,0.06010922,0.007151004,0.0487434,0.0001677872],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.3849705,0.007597604,0.4587806,0.003336426,0.001105552,0.001632164,0.09348853,0.03914281,0.009945844],"genre_scores_gemma":[0.6071891,0.001797467,0.2432656,0.0009817105,0.0002865425,0.001149845,0.1380037,0.0008553282,0.006470728],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.004783347,"threshold_uncertainty_score":0.01276541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04161346687462425,"score_gpt":0.2554672176071933,"score_spread":0.2138537507325691,"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."}}