{"id":"W2540583630","doi":"10.1109/nssmic.2012.6551696","title":"Performance assessment of motion correction for different distributions and count levels","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Engineering and Physical Sciences Research Council","keywords":"Positron emission tomography; Bootstrapping (finance); Position (finance); Computer science; Artificial intelligence; Motion (physics); Frame (networking); Computer vision; Mathematics; Nuclear medicine; Variance (accounting); Statistics; Accounting","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":[0.003601008,0.0008494241,0.0006313124,0.0009815113,0.0003744121,0.0007312846,0.0006852921,0.0008963237,0.0009559842],"category_scores_gemma":[0.01873106,0.0002478281,0.0005210484,0.0007681708,0.0003177259,0.0006063926,0.0007608744,0.0004533789,0.0005914454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003152015,"about_ca_system_score_gemma":0.0005855581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002691766,"about_ca_topic_score_gemma":0.002377211,"domain_scores_codex":[0.9981858,0.0006014751,0.0001844906,0.0004447125,0.0004415251,0.0001420097],"domain_scores_gemma":[0.990768,0.005251501,0.0008465161,0.001081012,0.001879087,0.0001738507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006571329,0.0003112385,0.02202259,0.0006778096,0.0006046494,0.0004615028,0.000769102,0.0784334,0.2175066,0.000798382,0.001196842,0.6706465],"study_design_scores_gemma":[0.0001302105,0.002609228,0.06776594,0.00007708492,0.0003968974,0.001316295,0.0002725589,0.6595827,0.2622002,0.0007882047,0.004657427,0.0002033057],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6403903,0.001820725,0.3513945,0.0001667073,0.0001098577,0.0001649365,0.0003734958,0.004312784,0.001266686],"genre_scores_gemma":[0.769046,0.000509212,0.226913,0.00005374819,0.00003754169,0.0001200908,0.00115923,0.0006112254,0.001549868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003601008,"threshold_uncertainty_score":0.01904422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04615683046208521,"score_gpt":0.3685611872675373,"score_spread":0.3224043568054522,"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."}}