{"id":"W2581160999","doi":"10.1109/tuffc.2017.2661238","title":"Photoacoustic–Ultrasound Tomography With S-Sequence Aperture Encoding","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research","keywords":"Imaging phantom; Coded aperture; Tomography; Optics; Aperture (computer memory); Encoding (memory); Ultrasound; Photoacoustic imaging in biomedicine; Image resolution; Diffraction; Physics; Materials science; Acoustics; Computer science; Artificial intelligence; Detector","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002528379,0.0005404604,0.0004823328,0.0002747815,0.0012558,0.0004918918,0.0005138273,0.0002302238,0.00006191919],"category_scores_gemma":[0.00008492505,0.0004822646,0.0001548911,0.0002995033,0.0002899251,0.0005217987,8.744785e-7,0.0009778213,0.000009992048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001600206,"about_ca_system_score_gemma":0.0001103818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001285294,"about_ca_topic_score_gemma":0.0001161766,"domain_scores_codex":[0.9977928,0.00003432582,0.0003626203,0.0005511171,0.0004028063,0.0008563393],"domain_scores_gemma":[0.9979022,0.000775495,0.0001261087,0.0007695467,0.0001282821,0.0002983855],"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":[0.0003314564,0.0004297336,0.002475589,0.0003549251,0.001438319,0.0003117652,0.0008738448,0.06317376,0.876435,0.0015322,0.0002962188,0.05234712],"study_design_scores_gemma":[0.008774281,0.001221235,0.002506391,0.0005585089,0.001270025,0.001476987,0.000291163,0.9341441,0.04357162,0.001732807,0.001081305,0.003371539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04523451,0.001075106,0.9456177,0.000136297,0.0006422106,0.0005999708,0.000360255,0.0005034862,0.005830416],"genre_scores_gemma":[0.9961537,0.001581138,0.001571293,0.0003285152,0.00006670727,0.0001206949,0.000006374791,0.00009708601,0.00007449564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9509192,"threshold_uncertainty_score":0.9997629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01010012591753773,"score_gpt":0.2108328524376721,"score_spread":0.2007327265201344,"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."}}