{"id":"W4200278843","doi":"10.3390/s21248172","title":"An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"University of Manitoba; Natural Sciences and Engineering Research Council of Canada; CancerCare Manitoba Foundation","keywords":"Artifact (error); Radar; Artificial intelligence; Computer science; Iterative reconstruction; Sensitivity (control systems); Feature (linguistics); Computer vision; Microwave; Microwave imaging; Imaging phantom; Algorithm; Pattern recognition (psychology); Medicine; Nuclear medicine; Electronic engineering; Engineering; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0001138278,0.0001388976,0.000178659,0.0002158589,0.00004470923,0.00009090782,0.00005431323,0.00005209103,0.0000232116],"category_scores_gemma":[0.00001388871,0.0001668397,0.0000563408,0.0005043689,0.00002272519,0.00007078833,0.000007820011,0.0001314054,0.00001720183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009244079,"about_ca_system_score_gemma":0.00002283831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006323662,"about_ca_topic_score_gemma":0.00002105685,"domain_scores_codex":[0.9991505,0.00006858917,0.0002026167,0.0002701891,0.00008416537,0.0002238976],"domain_scores_gemma":[0.9995223,0.00001678341,0.00001799775,0.0002885635,0.00006366759,0.00009065133],"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":[0.000002649724,0.00001386053,0.0002053341,0.00002181686,0.00001295005,0.00001672548,0.0001763078,0.8103697,0.1841608,9.982189e-7,0.00007236014,0.004946509],"study_design_scores_gemma":[0.0001640651,0.000002320232,0.0002187187,0.00003750386,0.00001503599,0.0002235935,0.000397712,0.9525991,0.04610592,0.000004616579,0.00003413142,0.0001972649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6296532,0.00002353236,0.3662688,0.0002330852,0.0001419423,0.00005728015,0.00001486462,0.0002281092,0.003379276],"genre_scores_gemma":[0.7025802,0.00000404039,0.2971757,0.00007395635,0.00004859956,8.237764e-7,0.00005170068,0.0000314618,0.00003344264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1422295,"threshold_uncertainty_score":0.6803527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00556312905979181,"score_gpt":0.1954394346800181,"score_spread":0.1898763056202263,"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."}}