{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003708399,0.0007566506,0.000542278,0.001777922,0.0004555996,0.0007047071,0.0008303741,0.0007572778,0.004782793],"category_scores_gemma":[0.02014749,0.0003122905,0.0002702858,0.001607934,0.001096296,0.001144865,0.001044534,0.0004971857,0.0003684952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004335166,"about_ca_system_score_gemma":0.0003643091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007963929,"about_ca_topic_score_gemma":0.000896352,"domain_scores_codex":[0.9985479,0.0004893392,0.0001791398,0.0001196253,0.0005692321,0.00009475867],"domain_scores_gemma":[0.9868504,0.008454573,0.0007328037,0.001521158,0.002192481,0.000248591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01297308,0.002845775,0.01097128,0.004811061,0.0004261733,0.0007844712,0.002390638,0.2614924,0.2542361,0.03740543,0.00925537,0.4024082],"study_design_scores_gemma":[0.0008458984,0.008670131,0.01487927,0.0002873787,0.0002909638,0.001331462,0.001428017,0.5485193,0.3913312,0.0163684,0.0157225,0.000325551],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6981217,0.002394942,0.2817238,0.0007426857,0.0003841204,0.0004782938,0.001040965,0.002403383,0.01271004],"genre_scores_gemma":[0.8142217,0.0009201283,0.181027,0.00009177704,0.00006645191,0.0004208803,0.0005941622,0.0003850425,0.002272866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004782793,"threshold_uncertainty_score":0.01961207,"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."}}