{"id":"W2023957719","doi":"10.1002/jmri.20144","title":"Quantitative evaluation of metal artifact reduction techniques","year":2004,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artifact (error); Distortion (music); Noise (video); Reduction (mathematics); Materials science; Noise reduction; Image quality; Signal-to-noise ratio (imaging); Artificial intelligence; Computer vision; Computer science; Image (mathematics); Optics; Mathematics; Physics","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":[0.0008118966,0.00008440918,0.0002320169,0.0001643646,0.00003415265,0.000006951816,0.00006821134,0.00002551457,0.00004547737],"category_scores_gemma":[0.0001980322,0.00007007326,0.0001060966,0.0002206814,0.0001059102,0.0002076794,0.00001255091,0.0001748736,0.000001629411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001412628,"about_ca_system_score_gemma":0.0001932163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000114573,"about_ca_topic_score_gemma":5.294166e-7,"domain_scores_codex":[0.9987673,0.00003437934,0.0004868824,0.0001034913,0.0005016079,0.0001063396],"domain_scores_gemma":[0.9984242,0.00002176247,0.0004301337,0.0001545223,0.0009216843,0.00004770329],"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":[0.0001378958,0.0001718764,0.0003021902,0.00001574027,0.000004912604,0.000008702844,0.0001949676,0.0002357079,0.3409862,0.001946469,0.000155425,0.6558399],"study_design_scores_gemma":[0.002638473,0.002025901,0.02687069,0.001252716,0.0006194908,0.002170371,0.001097027,0.002112912,0.8899521,0.05972997,0.01130862,0.0002216802],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7672926,0.03427331,0.186814,0.00608974,0.0001437209,0.001069867,0.000005697572,0.00007475867,0.00423631],"genre_scores_gemma":[0.7207386,0.0004758792,0.278622,0.00003407614,0.00007058014,0.00001464019,0.000001068383,0.00001070082,0.00003242095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6556183,"threshold_uncertainty_score":0.2857504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04054462545377114,"score_gpt":0.3845878771140973,"score_spread":0.3440432516603261,"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."}}