{"id":"W2069814604","doi":"10.1118/1.1758350","title":"Spektr: A computational tool for x‐ray spectral analysis and imaging system optimization","year":2004,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":279,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Medical imaging; Computer science; Medical physics; Physics; Artificial intelligence","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":[0.00007669986,0.00009500248,0.0001558852,0.00004099464,0.00006392973,0.00002585342,0.00005485812,0.00002442067,0.000009716836],"category_scores_gemma":[0.00002002305,0.00009559506,0.00006300733,0.0002433322,0.00003966504,0.0001434661,0.00001147625,0.00008941318,0.000002786712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007471126,"about_ca_system_score_gemma":0.00002030191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003330455,"about_ca_topic_score_gemma":9.00763e-7,"domain_scores_codex":[0.9993517,0.000004837469,0.0001492909,0.0001260922,0.0002065065,0.0001616121],"domain_scores_gemma":[0.9997572,0.00005160841,0.00002024113,0.00006669456,0.00002922407,0.00007502008],"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":[0.00000261549,0.00001145105,0.0003598331,0.00006661788,0.00008874035,0.000005188685,0.00009399623,0.9916412,0.00001757096,0.002108586,0.00001643856,0.005587721],"study_design_scores_gemma":[0.0005266186,0.000005324956,0.0003860697,0.00003859224,0.0001223237,0.000005564817,0.00004251418,0.9956234,0.0002585127,0.002838212,0.00003111606,0.000121757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01011445,0.0001038061,0.9890566,0.000114222,0.00009515294,0.00009385424,0.000009824024,0.0001846871,0.0002273917],"genre_scores_gemma":[0.9428883,0.000006134623,0.05670014,0.00006817372,0.000245377,0.00001140848,0.00005979796,0.00001652624,0.000004150176],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9327738,"threshold_uncertainty_score":0.3898253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003851405066694974,"score_gpt":0.2171818378365232,"score_spread":0.2133304327698282,"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."}}