{"id":"W3009496995","doi":"10.1109/tap.2020.3026427","title":"Phaseless Gauss-Newton Inversion for Microwave Imaging","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inversion (geology); Algorithm; Multiplicative function; Computer science; Phase retrieval; Microwave imaging; Regularization (linguistics); Mathematics; Microwave; Artificial intelligence; Mathematical analysis; Fourier transform","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008033051,0.0007743679,0.0004363335,0.0004578215,0.0003098152,0.0006396832,0.0009537284,0.0007269408,0.002440101],"category_scores_gemma":[0.002183496,0.0004150173,0.0005233191,0.0006261047,0.0004688207,0.0007537805,0.0008569966,0.001417464,0.001503254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004792198,"about_ca_system_score_gemma":0.001079388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001882973,"about_ca_topic_score_gemma":0.003055476,"domain_scores_codex":[0.9996405,0.00009529587,0.00001482967,0.00005370719,0.0001761829,0.00001959635],"domain_scores_gemma":[0.99949,0.000211785,0.00006626959,0.00007277742,0.0001386875,0.00002042882],"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":[0.0001082574,0.0000941653,0.0008420477,0.0003139708,0.00009275802,0.0001397629,0.0001764626,0.380269,0.04546393,0.1043768,0.01324221,0.4548806],"study_design_scores_gemma":[0.00000570972,0.00001420964,0.0001297635,0.000009104193,0.000004938689,0.00005830995,0.000007360478,0.9768163,0.006506959,0.007958974,0.008477616,0.00001077468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005780078,0.00004702979,0.9984524,0.00003575613,0.00001341443,0.00001323363,0.00001935599,0.0002048671,0.0006358559],"genre_scores_gemma":[0.02366837,0.0002466236,0.972976,0.00006524798,0.00003599678,0.0000962758,0.000192879,0.0001476821,0.002570899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002440101,"threshold_uncertainty_score":0.008162916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571928080834785,"score_gpt":0.216448053268544,"score_spread":0.2007287724601962,"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."}}