{"id":"W2119270633","doi":"10.1109/tmi.2006.879922","title":"Creation and Application of a Simulated Database of Dynamic [<tex>$^18$</tex>F]MPPF PET Acquisitions Incorporating Inter-Individual Anatomical and Biological Variability","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Context (archaeology); Ground truth; Database; Relevance (law); Data mining; Positron emission tomography; Artificial intelligence; Nuclear 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133046,0.0002582193,0.0005285964,0.0002770227,0.0001881501,0.00002449936,0.0001758301,0.0001692849,0.0002190618],"category_scores_gemma":[0.000236257,0.0002253175,0.0001095537,0.0004982902,0.001230208,0.0001562052,0.00002120655,0.000570995,0.000002847781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008063567,"about_ca_system_score_gemma":0.0001287904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001577076,"about_ca_topic_score_gemma":0.00001972803,"domain_scores_codex":[0.9974084,0.0001845905,0.000927182,0.0006026287,0.0005988906,0.0002783477],"domain_scores_gemma":[0.9980078,0.0006839473,0.0002657789,0.0004853561,0.0002144964,0.0003426259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00065235,0.00653711,0.01401139,0.001275052,0.0003280531,0.00009988044,0.0003220979,0.0002756686,0.671084,0.008354485,0.001725989,0.295334],"study_design_scores_gemma":[0.003126373,0.0002871006,0.01298323,0.0009355132,0.0005809837,0.0004372236,0.0001207856,0.9608833,0.01420346,0.005524945,0.0004695732,0.0004475449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3374678,0.00006005152,0.6586596,0.002723664,0.00003590636,0.000543504,0.0001797393,0.0001476804,0.0001820436],"genre_scores_gemma":[0.9751702,0.0001015543,0.02388984,0.0003273297,0.00005019536,0.00008673604,0.000331338,0.00002557231,0.00001722695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9606076,"threshold_uncertainty_score":0.9188183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283539233495675,"score_gpt":0.3075443881545458,"score_spread":0.2947089958195891,"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."}}