{"id":"W2006707584","doi":"10.1016/j.juro.2012.02.1646","title":"1709 IMPROVED DETECTION OF KIDNEY STONE TWINKLING USING AUTOREGRESSIVE SIGNAL PROCESSING METHOD","year":2012,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Renal and Vascular Pathologies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Blood flow; Power doppler; Kidney stones; Autocorrelation; Artifact (error); SIGNAL (programming language); Ultrasound; Artificial intelligence; Radiology; Surgery; Computer science; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001921875,0.0000995765,0.000360424,0.0001354277,0.00007695706,0.000003927622,0.0001014205,0.0001051507,0.00001669096],"category_scores_gemma":[0.0004749936,0.00005419129,0.0001364698,0.0001052256,0.0001182502,0.0001326039,0.00003951394,0.0004049634,9.044872e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003191909,"about_ca_system_score_gemma":0.0001691647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001246096,"about_ca_topic_score_gemma":3.879161e-7,"domain_scores_codex":[0.9987914,0.000369655,0.0003866102,0.00005763942,0.0001723625,0.0002223375],"domain_scores_gemma":[0.9987018,0.0001433203,0.0006629172,0.0001051251,0.0002480515,0.0001388304],"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":[0.0007452429,0.00006913982,0.001272833,0.00005202766,0.0001171476,0.00001323334,0.001229176,0.000289406,0.9621385,0.000003449153,0.000009943412,0.03405986],"study_design_scores_gemma":[0.004138236,0.003737581,0.02569558,0.0002908555,0.003086714,0.02769369,0.00135102,0.04152729,0.8902819,0.0006113867,0.001340214,0.0002455064],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7430867,0.004202003,0.2518763,0.000476645,0.0002090106,0.00009293021,6.142683e-7,0.000008790148,0.00004702272],"genre_scores_gemma":[0.9875901,0.00005297796,0.01145916,0.0003496769,0.0005179677,5.717195e-7,2.818101e-7,0.00001217,0.00001709769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2445034,"threshold_uncertainty_score":0.2209857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03422331429466206,"score_gpt":0.3319063477131525,"score_spread":0.2976830334184905,"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."}}