{"id":"W4246959343","doi":"10.36227/techrxiv.11831295.v1","title":"Phaseless Gauss-Newton Inversion for Microwave Imaging","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Manitoba","keywords":"Inversion (geology); Algorithm; Multiplicative function; Phase retrieval; Regularization (linguistics); Prior information; Computer science; Microwave imaging; Mathematics; Microwave; Artificial intelligence; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001406575,0.0004376541,0.000524282,0.0002101361,0.00007470491,0.0002026436,0.000403407,0.0001520206,0.00007112017],"category_scores_gemma":[0.00002305396,0.0004674403,0.0004132726,0.0001308481,0.00003570995,0.00006026688,0.000284633,0.0005215925,0.00008058535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001472819,"about_ca_system_score_gemma":0.00003438928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001003575,"about_ca_topic_score_gemma":0.00001378982,"domain_scores_codex":[0.998464,0.00002141513,0.0003829179,0.0005812243,0.0001495111,0.00040095],"domain_scores_gemma":[0.9991561,0.00005218607,0.00006824462,0.0004909557,0.00006818366,0.0001644006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002002495,0.00002850446,0.0003946963,0.002248841,0.0005242963,0.00002508771,0.0005962164,0.02461522,0.407499,0.00006605547,0.5451282,0.01885376],"study_design_scores_gemma":[0.0004702233,0.000007961944,0.00002251674,0.0002317389,0.0002807963,0.000006950236,0.0001284159,0.8012697,0.1519934,0.001263371,0.04349967,0.0008253051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0324684,0.001353216,0.9475527,0.005309061,0.001806327,0.0005459461,0.0001876499,0.002074732,0.008701995],"genre_scores_gemma":[0.9813577,0.0001557434,0.0158166,0.0005526175,0.0005573425,0.00006034918,0.0005134034,0.0001509656,0.0008352639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9488893,"threshold_uncertainty_score":0.9997777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969349978298262,"score_gpt":0.2408031502715301,"score_spread":0.2211096504885475,"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."}}