{"id":"W2119270633","doi":"10.1109/tmi.2006.879922","title":"Creation and Application of a Simulated Database of Dynamic [&lt;tex&gt;$^18$&lt;/tex&gt;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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002321188,0.0008246825,0.001088045,0.001697101,0.0004233905,0.001479803,0.002449172,0.001449315,0.004951485],"category_scores_gemma":[0.006690278,0.0006603386,0.001017889,0.001753497,0.0006108426,0.0008014075,0.001279101,0.0009652817,0.001439398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008498669,"about_ca_system_score_gemma":0.001539635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003182086,"about_ca_topic_score_gemma":0.003174501,"domain_scores_codex":[0.99899,0.0002425989,0.00009846722,0.0002814153,0.000313479,0.00007405978],"domain_scores_gemma":[0.9962037,0.001898671,0.0002270977,0.0008880242,0.0006459787,0.0001364532],"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":[0.001129376,0.0008965593,0.01298297,0.0006257005,0.0003681766,0.001164206,0.0004475831,0.8267267,0.03492678,0.006515269,0.00790319,0.1063136],"study_design_scores_gemma":[0.0001073741,0.0003659053,0.005464789,0.00004550342,0.00008998566,0.0006750463,0.0001002485,0.9417306,0.03526251,0.004635298,0.01141714,0.0001056667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3370373,0.0004249792,0.6322558,0.0003637336,0.0001758255,0.00121267,0.01752198,0.006510696,0.004497014],"genre_scores_gemma":[0.6160386,0.0005222433,0.3431967,0.0001856364,0.00004931632,0.002451061,0.03426819,0.001256561,0.002031693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004951485,"threshold_uncertainty_score":0.01656437,"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."}}