{"id":"W3041607742","doi":"10.23919/eucap48036.2020.9135659","title":"An Open-Access Experimental Dataset for Breast Microwave Imaging","year":2020,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Microwave imaging; Logistic regression; Computer science; Breast cancer; Imaging phantom; Artificial intelligence; Machine learning; Breast imaging; Medical imaging; Classifier (UML); Mammography; Medical physics; Data mining; Microwave; Medicine; Cancer; Radiology; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007360372,0.0001611843,0.0001849026,0.00004357495,0.00007657625,0.0009213077,0.0009959285,0.00001753133,0.0003678467],"category_scores_gemma":[0.000003392135,0.0001625489,0.00004123385,0.0001274701,0.00002253619,0.0008904387,0.0002327972,0.00006653481,0.0000465701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002769882,"about_ca_system_score_gemma":0.000008917903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001204852,"about_ca_topic_score_gemma":0.00001031545,"domain_scores_codex":[0.9992019,0.0000124671,0.0001768378,0.0003019726,0.00006340026,0.0002434061],"domain_scores_gemma":[0.9994757,0.00001162805,0.00001818348,0.0002966816,0.00001611933,0.0001816755],"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":[0.00001491633,0.00003063985,0.0003979226,0.0000354393,0.00004905038,0.000005946755,0.0002066036,0.001544201,0.8114979,0.00002094722,0.1804972,0.005699302],"study_design_scores_gemma":[0.0007764417,0.00003356518,0.0002415733,0.00001521929,0.00004799505,0.00005217636,0.0004850514,0.4911407,0.4772243,0.00002458139,0.02940005,0.0005583726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1982479,0.0009203895,0.7694831,0.007327039,0.0005353407,0.001145915,0.01380197,0.001765043,0.006773316],"genre_scores_gemma":[0.9897802,0.000003218045,0.006546253,0.001747115,0.0001314855,0.00002914251,0.001692805,0.00004826334,0.00002152649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7915323,"threshold_uncertainty_score":0.8884191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03969816747301863,"score_gpt":0.3360571915023514,"score_spread":0.2963590240293328,"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."}}