{"id":"W1985940535","doi":"10.1049/el:20056953","title":"Tissue sensing adaptive radar for breast cancer detection: study of immersion liquids","year":2005,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Breast cancer; Radar; Radar detection; Microwave; Wideband; Computer science; Electronic engineering; Biomedical engineering; Cancer; Medicine; Engineering; Telecommunications; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001144107,0.0001456616,0.0001982175,0.0001181324,0.00007779361,0.00001641716,0.00008349646,0.00003272125,0.00001313267],"category_scores_gemma":[0.000001619558,0.0001574781,0.00006767683,0.0001822085,0.00001612117,0.00006946898,0.00001152292,0.0001565856,0.000004054157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002690435,"about_ca_system_score_gemma":0.00001232148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000127685,"about_ca_topic_score_gemma":0.000253994,"domain_scores_codex":[0.9991705,0.00002308995,0.0001950912,0.0001830667,0.0001137824,0.0003144646],"domain_scores_gemma":[0.9996898,0.00002454668,0.00004093146,0.0001737559,0.00003530933,0.00003571785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000338803,0.00002390886,0.00002442629,0.00001808776,0.0001939085,0.000001056819,0.000476355,0.04661204,0.8129958,0.000001004532,0.00086586,0.1387537],"study_design_scores_gemma":[0.001097342,0.0002802905,0.0003143604,0.000046882,0.0002800864,0.00004041585,0.0005140724,0.1432834,0.8490576,0.00000345364,0.00463208,0.0004500271],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.898553,0.0006380259,0.09937242,0.00102776,0.0001093904,0.0001672429,0.00001061395,0.0001018646,0.00001966767],"genre_scores_gemma":[0.9985493,0.00004244974,0.0009513048,0.0001628177,0.0002147543,0.000008923604,0.000002317052,0.00003817258,0.00002989731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1383037,"threshold_uncertainty_score":0.6421771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005802468134080529,"score_gpt":0.2216604900538094,"score_spread":0.2158580219197289,"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."}}