{"id":"W2118212327","doi":"10.1109/tns.2004.834825","title":"Assessment of brain surface extraction from PET images using Monte Carlo Simulations","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Nuclear Science","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial intelligence; Monte Carlo method; Image registration; Positron emission tomography; Similarity (geometry); Computer vision; Computer science; Raclopride; Pattern recognition (psychology); Partial volume; Nuclear medicine; Mathematics; Image (mathematics); Chemistry; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002087699,0.0001033493,0.0001629294,0.0001326894,0.0003188276,0.00003419026,0.000153881,0.00003465937,0.0001715776],"category_scores_gemma":[0.00001995366,0.00009623593,0.00007522597,0.0005947573,0.0004392194,0.0002667246,0.000002448273,0.0002585472,0.000008489101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002142978,"about_ca_system_score_gemma":0.0002576856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009255331,"about_ca_topic_score_gemma":0.00001262508,"domain_scores_codex":[0.9987922,0.00001664397,0.0002331127,0.0003042973,0.0004763851,0.0001774163],"domain_scores_gemma":[0.9991447,0.00008175785,0.00008412363,0.0003892708,0.0001445513,0.0001555518],"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":[0.000008389477,0.0003281277,0.0000441313,0.000008226346,0.000009572869,0.000004937723,0.00009052474,0.1904965,0.8080462,0.0001090956,0.00005167544,0.0008026256],"study_design_scores_gemma":[0.0009651587,0.0002248089,0.007448157,0.0002515224,0.000127613,0.00006560856,0.0001913343,0.8559008,0.1338801,0.0002370405,0.0004915823,0.0002162821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7053418,0.000002740233,0.2916716,0.002410063,0.00008581828,0.0002012445,0.00004557484,0.0001020696,0.0001390954],"genre_scores_gemma":[0.8040546,0.0000112737,0.1956293,0.0002243692,0.00001531809,0.000002560566,4.406167e-7,0.00001336129,0.00004871083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6741661,"threshold_uncertainty_score":0.3924388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03109106090888953,"score_gpt":0.3704419501551479,"score_spread":0.3393508892462584,"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."}}