{"id":"W3215588322","doi":"10.1109/tuffc.2014.6805701","title":"S-sequence spatially-encoded synthetic aperture ultrasound imaging [Correspondence]","year":2014,"lang":"en","type":"letter","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ultrasonic imaging; Synthetic aperture radar; Sequence (biology); Ultrasound; Computer science; Physics; Optics; Artificial intelligence; Acoustics; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0006578608,0.00137464,0.001643458,0.001341292,0.0007673638,0.0003837042,0.0005481248,0.001321113,0.0003145448],"category_scores_gemma":[0.0004490939,0.001265694,0.0008788322,0.001153098,0.000585199,0.0002625002,0.000001421653,0.006814535,0.0001256965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004150898,"about_ca_system_score_gemma":0.0007103746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003731464,"about_ca_topic_score_gemma":0.00003895883,"domain_scores_codex":[0.9938118,0.0004170818,0.001065751,0.001759845,0.001285428,0.00166011],"domain_scores_gemma":[0.9928729,0.004355149,0.0004598835,0.001315592,0.0005141235,0.0004823315],"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":[0.003511328,0.003191687,0.004813732,0.004054058,0.007381251,0.003783465,0.002000808,0.001692614,0.4470506,0.0004554466,0.3541382,0.1679268],"study_design_scores_gemma":[0.01707209,0.005682847,0.0003089448,0.003903898,0.01091048,0.02500317,0.0001004417,0.05993765,0.003562477,0.003945073,0.8612705,0.008302419],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001171259,0.003323118,0.7994506,0.1889332,0.001856231,0.001802029,0.0008230592,0.0006095556,0.002031034],"genre_scores_gemma":[0.5975785,0.002166465,0.002504938,0.3945643,0.001009324,0.0002561236,0.0002600826,0.0003065617,0.001353747],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7969456,"threshold_uncertainty_score":0.9999754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01210744277973502,"score_gpt":0.2359048618350493,"score_spread":0.2237974190553143,"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."}}