{"id":"W2318566048","doi":"10.1016/j.cjca.2012.07.645","title":"712 Correlating Current Pathways With Myocardial Fiber Orientation Through Fusion of Data From Current Density and Diffusion Tensor Imaging","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Cardiovascular Disease and Adiposity","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Diffusion MRI; Coronal plane; Orientation (vector space); Medicine; Pixel; Nuclear magnetic resonance; Magnetic resonance imaging; Diffusion; Current (fluid); Biomedical engineering; Artificial intelligence; Anatomy; Geometry; Radiology; Physics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003591911,0.000136316,0.0005799355,0.0001069403,0.0001062498,0.00001100454,0.00009597703,0.00005940052,0.00001850318],"category_scores_gemma":[0.0001163698,0.0001060403,0.0001849084,0.00009089367,0.0001418694,0.0003081564,0.00006043359,0.0003303461,0.00000285689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006668137,"about_ca_system_score_gemma":0.0004231751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004792964,"about_ca_topic_score_gemma":0.00007335757,"domain_scores_codex":[0.99883,0.0001755617,0.0003140031,0.0001886932,0.0002153941,0.0002763339],"domain_scores_gemma":[0.9986192,0.00007384762,0.000221365,0.0003541171,0.0001917024,0.0005397754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001626147,0.000009859239,0.920706,0.00003656156,0.0003255672,0.00006252783,0.0004345744,0.00003137189,0.0006397171,0.00002438108,0.0006987199,0.07686805],"study_design_scores_gemma":[0.0017951,0.00007871952,0.9620385,0.0002804134,0.001742292,0.0008534996,0.0002727303,0.0000837738,0.0001031296,0.00004874301,0.03256803,0.0001350819],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673694,0.02393224,0.007039155,0.00008937073,0.0009911179,0.0001525574,0.0001305102,0.00000434854,0.0002912864],"genre_scores_gemma":[0.9969777,0.0005649644,0.0005307614,0.00004094236,0.001691336,8.048181e-7,0.0001785032,0.00001341346,0.000001529337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07673297,"threshold_uncertainty_score":0.4324199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03697354940937441,"score_gpt":0.2736978716187697,"score_spread":0.2367243222093953,"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."}}