{"id":"W2148353467","doi":"10.1667/rr2620.1","title":"Principles of Magnetic Resonance Imaging","year":2012,"lang":"en","type":"review","venue":"Radiation Research","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Magnetic resonance imaging; Nuclear magnetic resonance; Simple (philosophy); Medical imaging; Resonance (particle physics); Medical physics; Computer science; Physics; Medicine; Artificial intelligence; Radiology; Philosophy; Atomic physics; Epistemology","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":[],"consensus_categories":[],"category_scores_codex":[0.0008100669,0.0001302,0.000583751,0.0003708695,0.00007171567,0.000008838088,0.0001665906,0.0001169473,0.0002040902],"category_scores_gemma":[0.0002060437,0.0001077351,0.0001491281,0.000703998,0.0001277066,0.00004636466,0.00008341314,0.0005016337,0.000097181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001856996,"about_ca_system_score_gemma":0.0003293333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001219637,"about_ca_topic_score_gemma":5.602303e-7,"domain_scores_codex":[0.998437,0.0001159016,0.0004421994,0.0002353753,0.0004531939,0.000316284],"domain_scores_gemma":[0.9986414,0.0003192245,0.0001468907,0.0005518984,0.0002192656,0.0001212815],"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":[0.000002200574,0.00005025285,0.00004333971,0.002766405,0.000001884641,0.000001587269,0.00001184145,1.437345e-7,0.000002475614,0.006714919,0.0003791415,0.9900258],"study_design_scores_gemma":[0.00008182211,0.0000338794,0.0001709176,0.002720973,0.00008759857,0.00002289099,0.000005708123,0.00004326533,0.00001512578,0.00007990259,0.996659,0.00007885829],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[4.463135e-7,0.9877099,0.0005534609,0.0001184399,0.00001861325,0.001357467,0.00003314675,0.00004719534,0.01016128],"genre_scores_gemma":[0.00001450991,0.986513,0.008032463,0.000008950619,0.0002217832,0.0004984286,0.00007996162,0.00004052914,0.004590408],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9962799,"threshold_uncertainty_score":0.4393309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2481980412055356,"score_gpt":0.5175333940120954,"score_spread":0.2693353528065598,"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."}}