{"id":"W2162190703","doi":"10.1109/42.870663","title":"Using an MRI distortion transfer function to characterize the ghosts in motion-corrupted images","year":2000,"lang":"en","type":"review","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Distortion (music); Computer vision; Artificial intelligence; Motion (physics); Transfer function; Image (mathematics); Band-pass filter; Optical transfer function; Filter (signal processing); Computer science; Physics; Optics","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":[0.00130158,0.0007299655,0.001526771,0.002652335,0.0001957913,0.0008280527,0.001084522,0.001222346,0.001256522],"category_scores_gemma":[0.001899164,0.0002764276,0.0004099419,0.001648555,0.000807701,0.001222909,0.0003151937,0.0007594179,0.001980647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004987861,"about_ca_system_score_gemma":0.0005015884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001162207,"about_ca_topic_score_gemma":0.001232323,"domain_scores_codex":[0.999633,0.00006448916,0.00003357745,0.00007231741,0.0001783429,0.00001827139],"domain_scores_gemma":[0.9990515,0.0004591894,0.0001153024,0.00004788173,0.000301432,0.00002463579],"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":[0.00006694187,0.00004800123,0.0008589831,0.004399103,0.00006145942,0.0002587637,0.00006456648,0.0009086263,0.01415648,0.002886921,0.003025217,0.973265],"study_design_scores_gemma":[0.00007323245,0.0008896682,0.01399782,0.002656526,0.0004045725,0.02266321,0.0003417486,0.007869087,0.06757279,0.009332669,0.8739647,0.0002340515],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002098112,0.9727423,0.0216331,0.0003682723,0.0001722692,0.00002716902,0.00002698813,0.00008240625,0.002849312],"genre_scores_gemma":[0.0206231,0.9440528,0.02983405,0.0003997311,0.0002850817,0.00007539237,0.0001199205,0.00003683267,0.004573054],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002652335,"threshold_uncertainty_score":0.006883502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06343306108549765,"score_gpt":0.3813988969669664,"score_spread":0.3179658358814688,"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."}}