{"id":"W2100809985","doi":"10.1109/tmi.2002.803106","title":"Understanding phase maps in MRI: a new cutline phase unwrapping method","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced X-ray Imaging Techniques","field":"Physics and Astronomy","cited_by":160,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Undersampling; Phase unwrapping; Phase (matter); Computer science; Noise (video); Artificial intelligence; Computer vision; Image (mathematics); Algorithm; Synthetic aperture radar; Interferometry; Optics; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003862644,0.0002798914,0.0003329925,0.0003879396,0.0002121973,0.00006170393,0.0002658267,0.00005287549,0.002488739],"category_scores_gemma":[0.0000130845,0.0002914242,0.0001504995,0.0005632948,0.0001243324,0.0003985137,0.000003744318,0.0009287687,0.00003833316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001979012,"about_ca_system_score_gemma":0.00006947266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001834093,"about_ca_topic_score_gemma":0.000009261001,"domain_scores_codex":[0.9979573,0.0001145262,0.0004766555,0.0004653966,0.0004661826,0.0005199261],"domain_scores_gemma":[0.9989246,0.000265583,0.00009350746,0.0003191894,0.00002598845,0.000371187],"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":[0.00006335849,0.002253352,0.0001120535,0.00002209038,0.00008771367,0.0001215997,0.001108156,0.002328766,0.003756491,0.001466339,0.005715866,0.9829642],"study_design_scores_gemma":[0.01415876,0.0001279782,0.000001079471,0.0007018207,0.0001176235,0.00004222611,0.00177107,0.9104794,0.03120076,0.03070845,0.009839491,0.0008512971],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000403022,0.00006646336,0.9922067,0.004345019,0.0002842495,0.0002553567,0.00002707353,0.000317299,0.002094828],"genre_scores_gemma":[0.9134864,0.000032105,0.08526211,0.0005810854,0.0002464789,0.00004596288,0.000009674724,0.00005803666,0.0002781693],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9821129,"threshold_uncertainty_score":0.9999538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07668580059374637,"score_gpt":0.377082999521456,"score_spread":0.3003971989277096,"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."}}