{"id":"W2978053723","doi":"10.1002/jmri.26939","title":"Deep‐Learning Super‐Resolution MRI: Getting Something From Nothing","year":2019,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Context (archaeology); Nothing; Citation; Library science; Medicine; Computer science; Philosophy; History; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001292307,0.001002985,0.0008165112,0.000372954,0.0002618905,0.001445911,0.001167782,0.001968249,0.004388624],"category_scores_gemma":[0.005669749,0.0006950771,0.0004837472,0.0004520377,0.0008910173,0.004156508,0.001880255,0.003493591,0.002022752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000393034,"about_ca_system_score_gemma":0.0007670178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001917434,"about_ca_topic_score_gemma":0.003866644,"domain_scores_codex":[0.9996622,0.00007968381,0.00002228638,0.00006986716,0.0001289793,0.0000370889],"domain_scores_gemma":[0.9980087,0.0009136559,0.0001139801,0.0004169531,0.0003820697,0.0001647027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002592474,0.00009468041,0.001297258,0.0006127689,0.000206597,0.0001318109,0.0001077523,0.03941718,0.01794594,0.02610409,0.06152526,0.8522974],"study_design_scores_gemma":[0.00005048258,0.0001717371,0.00115538,0.0003316586,0.0001472013,0.0005543293,0.0001025531,0.6999202,0.02315807,0.2065156,0.06779207,0.0001007341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01297227,0.01946873,0.9410301,0.01601336,0.001330917,0.00003761835,0.000537367,0.003801179,0.004808378],"genre_scores_gemma":[0.259136,0.01884812,0.6929665,0.007007478,0.001976363,0.00008754087,0.001325463,0.0010342,0.01761832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004388624,"threshold_uncertainty_score":0.01468146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00807102936183993,"score_gpt":0.2780602922705481,"score_spread":0.2699892629087082,"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."}}