{"id":"W1976118866","doi":"10.1118/1.4887919","title":"SU‐D‐9A‐03: STAMP: Simulator for Texture Analysis in MRI/PET","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Imaging phantom; Monte Carlo method; Scanner; Artificial intelligence; Computer vision; MATLAB; Computer graphics (images); Simulation; Nuclear medicine; Mathematics","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":[0.000488344,0.0005330601,0.0003955469,0.0003809028,0.0001955788,0.000567054,0.001520974,0.000791076,0.009514743],"category_scores_gemma":[0.001341821,0.0004256524,0.000559727,0.0003348029,0.0002403223,0.0003588687,0.0003671555,0.000647914,0.001397383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000594608,"about_ca_system_score_gemma":0.0007933298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003355706,"about_ca_topic_score_gemma":0.002996975,"domain_scores_codex":[0.9998574,0.00003311082,0.00001101062,0.0000154813,0.00006462696,0.00001842156],"domain_scores_gemma":[0.9994618,0.0002562148,0.00005580892,0.00005994937,0.0001194801,0.00004672518],"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":[0.0005617442,0.0001707819,0.003758257,0.0004371736,0.0001034943,0.0004655969,0.0002278507,0.8895591,0.04221362,0.008462963,0.01337114,0.04066832],"study_design_scores_gemma":[0.00005286757,0.00004551003,0.0004021882,0.000008696938,0.000009485289,0.00007537351,0.000008245898,0.9860476,0.007411062,0.0005735222,0.005352197,0.00001321615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.1963754,0.0005010266,0.7440381,0.0003870581,0.0002252848,0.0004461011,0.006424839,0.02940486,0.02219741],"genre_scores_gemma":[0.6946133,0.000473838,0.2844741,0.0002056297,0.00004264701,0.0007606933,0.005223185,0.00452862,0.00967794],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.009514743,"threshold_uncertainty_score":0.03183001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007762166802469499,"score_gpt":0.3052439929228208,"score_spread":0.2974818261203513,"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."}}