{"id":"W4300841564","doi":"","title":"SOUND-SPEED AND ATTENUATION IMAGING OF BREAST TISSUE USING WAVEFORM TOMOGRAPHY OF TRANSMISSION ULTRASOUND DATA","year":2024,"lang":"en","type":"paratext","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Los Alamos National Laboratory; Natural Sciences and Engineering Research Council of Canada; National Nuclear Security Administration; U.S. Department of Energy","keywords":"Attenuation; Acoustics; Ultrasound; Waveform; Tomography; Speed of sound; Iterative reconstruction; Transmission (telecommunications); Breast imaging; Acoustic attenuation; Ultrasound imaging; Ultrasonic imaging; Computer science; Physics; Radiology; Optics; Medicine; Mammography; Breast cancer; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008976795,0.0003398668,0.0008745083,0.001238649,0.0001084658,0.00004944714,0.0003375559,0.0002736769,0.0002348281],"category_scores_gemma":[0.00007805597,0.0002771861,0.0001480063,0.001073886,0.001557009,0.0007038399,0.0002019911,0.0002621815,0.000005535882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003828543,"about_ca_system_score_gemma":0.0002339201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008858013,"about_ca_topic_score_gemma":0.00000334633,"domain_scores_codex":[0.9967689,0.00003357753,0.001528774,0.0004060988,0.001001127,0.0002615532],"domain_scores_gemma":[0.9972255,0.0001785919,0.0009932132,0.0007967857,0.0006427234,0.0001632096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004337633,0.003387491,0.01021328,0.05073975,0.003815266,0.00003448881,0.0006138774,0.0005991363,0.6563528,0.01587982,0.1366278,0.1173987],"study_design_scores_gemma":[0.02130648,0.006210954,0.2112061,0.06182051,0.01765408,0.005808448,0.001011382,0.004788333,0.293012,0.004572677,0.3672401,0.005368869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7819595,0.0210074,0.03646153,0.0007623743,0.00340597,0.003017778,0.01487562,0.0001792955,0.1383305],"genre_scores_gemma":[0.9764604,0.0006597465,0.003704873,0.00003708069,0.00006108337,0.00000591192,0.01867284,0.00002547245,0.0003725985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3633407,"threshold_uncertainty_score":0.9999681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834956522411627,"score_gpt":0.2773387317737167,"score_spread":0.2589891665496004,"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."}}