{"id":"W2966019324","doi":"10.1109/incap.2018.8770920","title":"Near-field Microwave Breast Cancer Detection using Electrically Small Sensors and Machine Intelligence","year":2018,"lang":"en","type":"article","venue":"2018 IEEE Indian Conference on Antennas and Propogation (InCAP)","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Microwave; Breast cancer; Antenna (radio); Magnetic field; Electromagnetic field; Capacitor; Near and far field; Reflection coefficient; Materials science; Biomagnetism; Optoelectronics; Reflection (computer programming); Nuclear magnetic resonance; Optics; Physics; Cancer; Electrical engineering; Computer science; Engineering; Medicine; Voltage; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001863217,0.0003214035,0.0003012366,0.0002965576,0.000114474,0.0003246753,0.0005619751,0.0006142638,0.000795607],"category_scores_gemma":[0.0004939234,0.0001685097,0.0002421961,0.0002023236,0.0003636185,0.0007222586,0.000280366,0.0002385658,0.0003844894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466477,"about_ca_system_score_gemma":0.00009313528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001046507,"about_ca_topic_score_gemma":0.0002080562,"domain_scores_codex":[0.9997908,0.00005149997,0.00000735475,0.00005918515,0.00007871905,0.00001239628],"domain_scores_gemma":[0.9998035,0.00009286755,0.00003447473,0.00002648962,0.00003193105,0.00001079612],"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":[0.0002294045,0.0001030343,0.001442917,0.0002344996,0.00003852613,0.0002124942,0.00006225424,0.007718903,0.8603221,0.004050677,0.0004351355,0.1251501],"study_design_scores_gemma":[0.00005229157,0.00164083,0.005927049,0.00003715571,0.0001256507,0.002397807,0.0001249759,0.2871583,0.6759623,0.005130017,0.02135379,0.00008985001],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1839563,0.003174511,0.8060271,0.0005140791,0.0001847449,0.00007412577,0.00005412286,0.0007148525,0.005300178],"genre_scores_gemma":[0.8033706,0.0008736274,0.1920981,0.0003174877,0.0001089368,0.00005720257,0.0000593994,0.00003480159,0.00307981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000795607,"threshold_uncertainty_score":0.002661586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456300535390491,"score_gpt":0.2431091949595154,"score_spread":0.2185461896056105,"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."}}