{"id":"W2019224463","doi":"10.1002/mrm.21187","title":"Correction for geometric distortion and N/2 ghosting in EPI by phase labeling for additional coordinate encoding (PLACE)","year":2007,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ghosting; Encoding (memory); Distortion (music); Computer science; Phase (matter); Artificial intelligence; Computer vision; Physics; Telecommunications","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":[0.002262976,0.0001126769,0.0001974012,0.0005339954,0.00008320691,0.00002625241,0.0001572167,0.00007273356,0.00002783223],"category_scores_gemma":[0.001898789,0.0001118581,0.00001773475,0.0008955998,0.00006040099,0.0002201025,0.00003068331,0.0001440733,4.223018e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001198991,"about_ca_system_score_gemma":0.00002716062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000632001,"about_ca_topic_score_gemma":0.00009341852,"domain_scores_codex":[0.998703,0.00003782988,0.00044928,0.0003387654,0.0002037154,0.0002674318],"domain_scores_gemma":[0.997925,0.001669921,0.0001317362,0.0001254005,0.00009599254,0.00005197193],"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":[0.0000887184,0.00008419486,0.001179135,0.00003564159,8.241752e-7,0.000003908716,0.0006592261,0.00002526352,0.001519281,0.0007306493,0.03768543,0.9579877],"study_design_scores_gemma":[0.01293496,0.004067018,0.02962766,0.001614419,0.00001655446,0.00004872417,0.0004595538,0.5961186,0.008484266,0.006601938,0.3393442,0.0006820912],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03861661,0.004718839,0.9538229,0.001292977,0.0003689602,0.0007426576,0.00002772885,0.00008159375,0.000327706],"genre_scores_gemma":[0.9175697,0.0003224871,0.0794352,0.0007546124,0.000213991,0.0004190171,0.0001789487,0.00002097147,0.001085088],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9573056,"threshold_uncertainty_score":0.4561441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01942087426703802,"score_gpt":0.3156871707508114,"score_spread":0.2962662964837734,"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."}}