{"id":"W2922001838","doi":"10.1117/12.2512870","title":"Constructing an average geometry and diffusion tensor magnetic resonance field from freshly explanted porcine hearts","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Tech University","funders":"","keywords":"Diffusion MRI; Atlas (anatomy); Tensor (intrinsic definition); Tensor field; Cardiac cycle; Diffusion; Magnetic resonance imaging; Structure tensor; Transformation (genetics); Physics; Computer science; Geometry; Nuclear magnetic resonance; Mathematics; Mathematical analysis; Artificial intelligence; Chemistry; Anatomy; Exact solutions in general relativity","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":[0.0003850495,0.000562545,0.0004332023,0.0006839386,0.0002965466,0.0007660303,0.0006613567,0.0005192526,0.001201542],"category_scores_gemma":[0.001130364,0.0006024693,0.0007046937,0.0004748561,0.0004562683,0.0005235463,0.0006565952,0.0007510214,0.0006880981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003319068,"about_ca_system_score_gemma":0.001129706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001984986,"about_ca_topic_score_gemma":0.003856244,"domain_scores_codex":[0.9998568,0.00001692862,0.00001196936,0.00005449658,0.0000473717,0.00001254435],"domain_scores_gemma":[0.9997111,0.000068152,0.00006057276,0.00007516156,0.00006100315,0.00002395319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001364645,0.00005723608,0.002816261,0.0002387609,0.00007482862,0.0006650322,0.0002794284,0.3179616,0.4377484,0.01355134,0.002695701,0.223775],"study_design_scores_gemma":[0.00001518082,0.0002725612,0.005864148,0.00002650917,0.00004744972,0.001113784,0.0001065793,0.8454627,0.1188759,0.0161614,0.01195331,0.00010042],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05778313,0.00009211819,0.9394261,0.00009802088,0.00003757762,0.00006177249,0.0004209789,0.001223906,0.0008564643],"genre_scores_gemma":[0.2204088,0.0005220569,0.7756115,0.00005152307,0.00003484245,0.0001532791,0.001444053,0.0004977959,0.001276219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001984986,"threshold_uncertainty_score":0.004019558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02608619032402877,"score_gpt":0.2955219663026206,"score_spread":0.2694357759785918,"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."}}