{"id":"W4393764803","doi":"10.5281/zenodo.7395772","title":"Dataset related to the article \"Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment\"","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Magnetic resonance imaging; Cardiac magnetic resonance; Artificial intelligence; Nuclear magnetic resonance; Cartography; Cardiology; Medicine; Computer science; Physics; Geography; Radiology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009875995,0.001743003,0.001436936,0.002470469,0.0007323307,0.001165438,0.002464247,0.002316518,0.02473947],"category_scores_gemma":[0.003994924,0.0003212402,0.001444337,0.002481324,0.0005521381,0.0005524026,0.001394673,0.00107653,0.01667153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009184653,"about_ca_system_score_gemma":0.002097296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01181766,"about_ca_topic_score_gemma":0.02488897,"domain_scores_codex":[0.999113,0.0001367327,0.0001882932,0.0002207098,0.000254594,0.00008659747],"domain_scores_gemma":[0.9978009,0.0007879621,0.0002609656,0.0004557479,0.0005483897,0.0001460178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001320142,0.0007004098,0.008369088,0.004654056,0.0003036452,0.0009563466,0.00006555855,0.004272205,0.005421385,0.0007447715,0.90677,0.06642256],"study_design_scores_gemma":[0.002327196,0.001195928,0.05207203,0.00108327,0.0006224661,0.003623625,0.0003126454,0.03268078,0.01811974,0.003257343,0.8844284,0.0002765116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008681341,0.0009382526,0.002682577,0.0005934279,0.0003776243,0.0006983065,0.9815996,0.00189762,0.002531269],"genre_scores_gemma":[0.009626829,0.0003027782,0.003745444,0.000190397,0.00007404185,0.0006976845,0.9835051,0.0000722556,0.00178557],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02473947,"threshold_uncertainty_score":0.08276176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389651914009006,"score_gpt":0.2325677543537255,"score_spread":0.2186712352136354,"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."}}