{"id":"W136966946","doi":"10.1109/tci.2015.2498402","title":"Undersampled Phase Retrieval With Outliers","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Imaging","topic":"Advanced X-ray Imaging Techniques","field":"Physics and Astronomy","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Israel Science Foundation","keywords":"Computer science; Outlier; Phase retrieval; Computer vision; Artificial intelligence; Phase (matter); Remote sensing; Mathematics; Geology; Physics; Fourier transform","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001027562,0.0001744789,0.0001414556,0.0001466508,0.0001675917,0.00007120946,0.0001154442,0.00001211166,0.00007113349],"category_scores_gemma":[0.000001672108,0.0001700448,0.00006110043,0.0002514381,0.0001086194,0.0003552998,0.000001192166,0.0002268857,0.00003679973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000098163,"about_ca_system_score_gemma":0.000157517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004206643,"about_ca_topic_score_gemma":8.603281e-7,"domain_scores_codex":[0.9989907,0.00003463453,0.0001787812,0.0002697587,0.0003106674,0.0002154781],"domain_scores_gemma":[0.9993135,0.0001100031,0.00007498745,0.000156085,0.0002048449,0.0001405208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004327306,0.0007564191,0.0006756673,0.000006540644,0.0001629204,0.00001396211,0.0007185639,0.9403781,0.0003797536,0.002892234,0.001101942,0.05248113],"study_design_scores_gemma":[0.01301469,0.0004865361,0.0001088109,0.0001409124,0.0002132093,0.00005114597,0.002718569,0.7943692,0.02910661,0.1555445,0.002915504,0.001330292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008782248,0.000006231433,0.9884041,0.0006549796,0.0001645033,0.0001830566,0.00004885632,0.0002738052,0.001482246],"genre_scores_gemma":[0.8964417,1.300703e-7,0.1031417,0.0001789918,0.00006224522,0.00001444571,0.00002823106,0.00003121843,0.00010141],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8876594,"threshold_uncertainty_score":0.6934224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02960783974726054,"score_gpt":0.3191181555736223,"score_spread":0.2895103158263618,"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."}}