{"id":"W2097827025","doi":"10.1109/aps.2007.4395959","title":"Efficient microwave breast imaging technique using parallel finite difference time domain and parallel genetic algorithms","year":2007,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"Mitacs","keywords":"Microwave imaging; Computer science; Finite-difference time-domain method; Message Passing Interface; Genetic algorithm; Parallel algorithm; Nonlinear system; Breast cancer; Domain (mathematical analysis); Iterative reconstruction; Algorithm; Parallel computing; Microwave; Message passing; Computer vision; Cancer; Mathematics; Optics; Machine learning; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003766915,0.0003215977,0.0003074049,0.0002723556,0.000127769,0.0001111941,0.0001723888,0.00007361497,0.00005628342],"category_scores_gemma":[0.000006454462,0.0003093727,0.00009574683,0.000263068,0.0001085831,0.00002993584,0.00008479253,0.0002167685,0.000039417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009083541,"about_ca_system_score_gemma":0.0000114603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001466815,"about_ca_topic_score_gemma":0.000008958547,"domain_scores_codex":[0.9984449,0.00003072203,0.0003854576,0.0003803936,0.0001688209,0.0005896875],"domain_scores_gemma":[0.9993252,0.00007885696,0.00004604968,0.0003347813,0.00003917523,0.0001759258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001088187,0.00004087019,0.003094789,0.0000719963,0.00009008461,0.0001310339,0.0003340331,0.1067627,0.8704876,0.00001634915,0.0001654266,0.01879423],"study_design_scores_gemma":[0.0003092078,0.000006890501,0.007445127,0.00008310674,0.0000514837,0.0008612108,0.00007172822,0.9848773,0.005514233,0.0001785245,0.00008883388,0.000512281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2891268,0.0004209465,0.7092553,0.00005998773,0.00003849921,0.0001239127,0.00000765548,0.0002977157,0.0006690871],"genre_scores_gemma":[0.7166889,0.00001607468,0.2829337,0.00007086821,0.00005681373,0.000007217916,0.000006163535,0.00004807743,0.0001722345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8781146,"threshold_uncertainty_score":0.9999359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006369145884670155,"score_gpt":0.2088384757857058,"score_spread":0.2024693299010356,"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."}}