{"id":"W1998821710","doi":"10.1002/mrm.1145","title":"SMASH and SENSE: Experimental and numerical comparisons","year":2001,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Research Resources; Natural Sciences and Engineering Research Council of Canada","keywords":"Artifact (error); Acceleration; Sense (electronics); Noise (video); Computer science; Cartesian coordinate system; Simple (philosophy); Artificial intelligence; Computer vision; Mathematics; Image (mathematics); Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009324101,0.0001218208,0.0003013927,0.00006708921,0.00004513562,0.000004123217,0.00003141039,0.00005327991,0.000173657],"category_scores_gemma":[0.00004349922,0.00009751982,0.00001158877,0.0002103047,0.0003118083,0.00002537532,0.00004147966,0.0001722285,0.000003192978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002547515,"about_ca_system_score_gemma":0.00001025559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007868469,"about_ca_topic_score_gemma":0.000004892825,"domain_scores_codex":[0.9991473,0.00001520288,0.0002302086,0.0002655738,0.0001477065,0.000193998],"domain_scores_gemma":[0.9995562,0.00006274058,0.00003127919,0.0001949235,0.000019688,0.000135118],"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":[0.0005417213,0.0006811811,0.4506654,0.00007700607,0.000004618109,0.0008144269,0.001622848,0.000005035079,0.04269784,0.007530455,0.02765096,0.4677085],"study_design_scores_gemma":[0.00310684,0.001316781,0.4480306,0.0003269551,0.00002521384,0.001275762,0.0009092354,0.004646887,0.0009257494,0.0005909933,0.5386568,0.0001882747],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8808309,0.07473999,0.01524536,0.01680457,0.0000475941,0.0008850592,0.000002756755,0.0001317121,0.01131202],"genre_scores_gemma":[0.9736256,0.002498104,0.0214124,0.001086484,0.0001095094,0.00008760826,0.000005669012,0.00001565957,0.001158974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5110058,"threshold_uncertainty_score":0.3976743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02718600493260865,"score_gpt":0.3469437628132187,"score_spread":0.3197577578806101,"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."}}