{"id":"W2139631152","doi":"10.1109/ultsym.2007.627","title":"P6C-7 Ultrasound RF Time Series for Detection of Prostate Cancer: Feature Selection and Frame Rate Analysis","year":2007,"lang":"en","type":"article","venue":"Proceedings/Proceedings - IEEE Ultrasonics Symposium","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Radio frequency; Artificial intelligence; Prostate cancer; Pattern recognition (psychology); Feature (linguistics); Frame rate; Frame (networking); Ultrasound; Feature selection; Series (stratigraphy); Ultrasonic sensor; Echo (communications protocol); Cancer; Computer vision; Medicine; Radiology; Telecommunications; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001212146,0.0005482198,0.0005930261,0.00232759,0.0002513564,0.00050978,0.0002712795,0.0005592678,0.0007422249],"category_scores_gemma":[0.004482734,0.0001147877,0.0003493795,0.0008728166,0.0002114434,0.0003487859,0.0001674227,0.0002954902,0.0003346392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003896533,"about_ca_system_score_gemma":0.0002757887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002333282,"about_ca_topic_score_gemma":0.001896795,"domain_scores_codex":[0.9994294,0.000119594,0.00003698068,0.00008598397,0.0002757497,0.00005241863],"domain_scores_gemma":[0.9983703,0.000946795,0.0001771429,0.00009158013,0.0003466155,0.00006761923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001839715,0.000364439,0.03536249,0.0002404341,0.0001782224,0.000329731,0.0001705766,0.03405964,0.2183465,0.0007754064,0.00241918,0.7059137],"study_design_scores_gemma":[0.00003725145,0.0007603628,0.1413486,0.00003360032,0.0001731674,0.001334535,0.00009643861,0.692847,0.1601878,0.0005137919,0.002572299,0.00009508556],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7577509,0.001251169,0.2373426,0.0002216677,0.00007699083,0.0001228358,0.0004732491,0.001287161,0.001473503],"genre_scores_gemma":[0.9034023,0.0004222835,0.09478926,0.00003014586,0.0000565573,0.0001056835,0.0005414906,0.00006011921,0.000592064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002333282,"threshold_uncertainty_score":0.006410539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005347006532032316,"score_gpt":0.2429966773085497,"score_spread":0.2376496707765174,"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."}}