{"id":"W2886901637","doi":"10.1038/s41598-018-31046-9","title":"Near Field Breast Tumor Detection Using Ultra-Narrow Band Probe with Machine Learning Techniques","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Environmental Studies Association of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Modality (human–computer interaction); Breast tissue; Computer science; Human breast; Field (mathematics); Microwave; Artificial intelligence; Chicken breast; Breast tumor; Biomedical engineering; Materials science; Breast cancer; Medicine; Mathematics; Biology; Telecommunications; Cancer","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":[0.0005936517,0.0001511157,0.0001547339,0.0001639752,0.0004836291,0.000427819,0.00007399564,0.00003793235,0.00008798718],"category_scores_gemma":[0.00003182967,0.0001296203,0.00005473491,0.0004826268,0.0001670495,0.000159668,0.00001179747,0.0001927288,0.00001031608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000498148,"about_ca_system_score_gemma":0.00002737743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002169952,"about_ca_topic_score_gemma":0.00008729073,"domain_scores_codex":[0.9988135,0.00002429467,0.0002570096,0.0004100793,0.0002245265,0.0002706479],"domain_scores_gemma":[0.9992968,0.00001036285,0.00009472879,0.0004109496,0.0001132131,0.00007387264],"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":[0.00000645096,0.000008720356,0.007359955,0.00003838021,0.00002285328,0.0001273804,0.0002312688,0.002144313,0.9862302,1.10593e-7,0.0002655523,0.003564811],"study_design_scores_gemma":[0.00003647065,0.00005297824,0.0001326231,0.00007871626,0.00003683103,0.003677887,0.00002102702,0.0421975,0.9485785,0.00004291838,0.004941726,0.000202867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698662,0.0000715592,0.02750096,0.00002913676,0.000856306,0.0001068785,0.000001058647,0.0006131771,0.0009547676],"genre_scores_gemma":[0.9941224,5.88485e-7,0.005136093,0.00001277421,0.0001075534,0.000006351807,0.000007443261,0.00003109015,0.0005757037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04005318,"threshold_uncertainty_score":0.5285764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005546560106396066,"score_gpt":0.2023885742791948,"score_spread":0.1968420141727988,"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."}}