{"id":"W2134375305","doi":"10.1118/1.1414309","title":"Automated seed detection and three‐dimensional reconstruction. II. Reconstruction of permanent prostate implants using simulated annealing","year":2001,"lang":"en","type":"article","venue":"Medical Physics","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Hôtel-Dieu de Québec; Centre hospitalier de l'Université Laval; Centre hospitalier universitaire de Québec","funders":"","keywords":"Simulated annealing; Matching (statistics); Iterative reconstruction; Computer science; Algorithm; Artificial intelligence; Calibration; 3D reconstruction; Radiography; Computer vision; Mathematics; Medicine; Radiology; Statistics","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":[0.000850397,0.0005706123,0.0005373824,0.0004961625,0.000314635,0.000681461,0.0007604608,0.001071821,0.001317736],"category_scores_gemma":[0.00251517,0.0008374713,0.0007944918,0.0004099809,0.0006911363,0.0004437584,0.0006437037,0.0005710309,0.0005719257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005597376,"about_ca_system_score_gemma":0.0008098662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001797944,"about_ca_topic_score_gemma":0.002077522,"domain_scores_codex":[0.9995615,0.0001406959,0.00002420997,0.00007268046,0.0001683975,0.00003255137],"domain_scores_gemma":[0.9992726,0.0004028816,0.00009149128,0.000129102,0.00008396049,0.00001991976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004676603,0.00009625449,0.00274017,0.0002723661,0.0001521464,0.0001975779,0.0003263572,0.5955845,0.09280211,0.00930443,0.001768338,0.2962882],"study_design_scores_gemma":[0.0000302505,0.00005892737,0.0007361849,0.000009736726,0.00001550379,0.0001950983,0.00001468659,0.9749668,0.02004664,0.002034185,0.001873838,0.00001816842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01521081,0.0001076832,0.9834023,0.00004187242,0.00001268836,0.00003610083,0.00001993141,0.0006351601,0.0005334908],"genre_scores_gemma":[0.1746972,0.00008889254,0.8239726,0.00002428009,0.00000635315,0.000109618,0.00006931234,0.0001523737,0.0008794108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001797944,"threshold_uncertainty_score":0.004497349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0208122439588303,"score_gpt":0.2812063897385602,"score_spread":0.2603941457797299,"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."}}