{"id":"W1982675592","doi":"10.1118/1.1617353","title":"Fundamental image quality limits for microcomputed tomography in small animals","year":2003,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":208,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Electric (Canada); Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Lawson Health Research Institute","keywords":"Scanner; Imaging phantom; Voxel; Isotropy; Attenuation coefficient; Image quality; Attenuation; Image resolution; Optics; Iterative reconstruction; Noise (video); Image noise; Physics; Materials science; Computer science; Image (mathematics); Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006214778,0.0001452269,0.0003440574,0.00004868803,0.00005133219,0.00001475097,0.000139637,0.0001163873,0.000117364],"category_scores_gemma":[0.0003796224,0.0001261391,0.0001592945,0.000358324,0.0002065455,0.00002772673,0.00002795373,0.0003173075,0.00001446809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004380083,"about_ca_system_score_gemma":0.0001359459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003113858,"about_ca_topic_score_gemma":0.000007368576,"domain_scores_codex":[0.9986235,0.00005231451,0.0003887897,0.0003108866,0.0003019965,0.0003224756],"domain_scores_gemma":[0.9990137,0.0002151979,0.00006887774,0.0002693248,0.00007623424,0.0003566303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004525571,0.01124089,0.09346902,0.002163601,0.0003018701,0.0001684331,0.0008026619,5.326041e-7,0.2438752,0.273035,0.1235841,0.2509062],"study_design_scores_gemma":[0.01864179,0.001396413,0.04945207,0.001573863,0.0003015798,0.0001454042,0.000260692,0.003461126,0.4890065,0.1607136,0.2733995,0.001647439],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4433244,0.0001702901,0.5412686,0.009909029,0.0001075392,0.001575827,0.00003391461,0.0002941012,0.003316327],"genre_scores_gemma":[0.8970727,0.00003421286,0.09874243,0.003480076,0.0002364675,0.0002410373,0.00007360217,0.00002898321,0.00009053337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4537483,"threshold_uncertainty_score":0.5143805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05874475636355419,"score_gpt":0.3715551576740505,"score_spread":0.3128104013104963,"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."}}