{"id":"W2036192730","doi":"10.1118/1.2961444","title":"SU‐GG‐I‐46: An Automatic Region Detection Algorithm for Analyzing Module 1 of the ACR CT Accreditation Phantom","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Imaging phantom; Canny edge detector; Scanner; Computer science; Computer vision; Artificial intelligence; Hough transform; Nuclear medicine; Algorithm; Edge detection; Image processing; Image (mathematics); Medicine","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":[0.001967608,0.001008313,0.0005259966,0.001640549,0.000303389,0.000983909,0.00110263,0.0008881785,0.002405554],"category_scores_gemma":[0.002662487,0.0005345917,0.0005240336,0.0009111896,0.0004132621,0.0007174644,0.0004727825,0.0006972791,0.001534809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005592347,"about_ca_system_score_gemma":0.0008869163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00111876,"about_ca_topic_score_gemma":0.001868154,"domain_scores_codex":[0.9990479,0.0002074171,0.00005941932,0.0001455992,0.0004592636,0.00008039203],"domain_scores_gemma":[0.998947,0.0003171457,0.0002064275,0.0001472582,0.000333813,0.00004852097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000781811,0.0001166284,0.004759172,0.0002838204,0.00009128463,0.0002371931,0.0001521516,0.02178778,0.4777011,0.002853438,0.006414088,0.4848216],"study_design_scores_gemma":[0.00007115509,0.0004951648,0.01081949,0.00003717671,0.000078196,0.001471112,0.00003808186,0.4476125,0.5168934,0.0009454334,0.02140263,0.0001357253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03454066,0.0005939538,0.9541221,0.00009266276,0.00003609852,0.000143121,0.0001825182,0.009164017,0.001124786],"genre_scores_gemma":[0.098672,0.0002088747,0.8977374,0.00007739215,0.0000169628,0.000205467,0.0006051979,0.0007326223,0.001744027],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002405554,"threshold_uncertainty_score":0.01040584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01626094496587798,"score_gpt":0.2516651821371546,"score_spread":0.2354042371712766,"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."}}