{"id":"W7057214066","doi":"","title":"Impact of algorithm, iterations, post-smoothing, count level and tracer distribution on single-frame positrom emission tomography quantification using a generalized image space reconstruction algorithm","year":2012,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Imaging phantom; Positron emission tomography; Iterative reconstruction; Reconstruction algorithm; Medical imaging; Noise (video); Tomography; Image resolution","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005272411,0.0009909152,0.000730249,0.0007592027,0.0003386355,0.001453465,0.0006799776,0.001386914,0.000730908],"category_scores_gemma":[0.02232011,0.0002890903,0.0006851449,0.0007044929,0.000590465,0.0008610114,0.0005829142,0.000587762,0.0002987791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004239062,"about_ca_system_score_gemma":0.0008538821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001670845,"about_ca_topic_score_gemma":0.00107996,"domain_scores_codex":[0.9971819,0.001106062,0.0003151773,0.0004958621,0.0007139812,0.0001871532],"domain_scores_gemma":[0.9851944,0.01105418,0.0008561255,0.0009830219,0.001748797,0.0001634439],"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":[0.005642302,0.0006384667,0.02275342,0.00131207,0.0009219911,0.0005102745,0.0007062217,0.4742785,0.1018205,0.002884748,0.0009783449,0.3875531],"study_design_scores_gemma":[0.0001557031,0.003228907,0.01978123,0.000212855,0.0007467126,0.0007823537,0.0002720814,0.8289773,0.1408388,0.001478176,0.003352907,0.0001729154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7070224,0.003371831,0.2851099,0.0002225414,0.0001260776,0.0001913498,0.0001589699,0.001785159,0.002011861],"genre_scores_gemma":[0.7831407,0.001196158,0.2135362,0.00008289555,0.00002656861,0.0001757067,0.0002834197,0.0006251844,0.0009330776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005272411,"threshold_uncertainty_score":0.02788347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007566522006213772,"score_gpt":0.1998489420267849,"score_spread":0.1922824200205711,"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."}}