{"id":"W2005248683","doi":"10.1118/1.2828186","title":"Dual-energy imaging of the chest: Optimization of image acquisition techniques for the ‘bone-only’ image","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Medical imaging; Image processing; Image (mathematics); Dual energy; Computer science; Image quality; Energy (signal processing); Computer vision; Medical physics; Radiology; Artificial intelligence; Nuclear medicine; Medicine; Physics; Osteoporosis; Pathology","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":[0.0001294697,0.000108057,0.0001560825,0.0000175005,0.0001018363,0.000005539375,0.0001673002,0.00003785639,0.00002109218],"category_scores_gemma":[0.00007453072,0.00007244262,0.0001008361,0.0001807387,0.0002896304,0.0001999761,0.00004821963,0.0001254212,5.268458e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002329179,"about_ca_system_score_gemma":0.00003511444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008010436,"about_ca_topic_score_gemma":5.344393e-7,"domain_scores_codex":[0.9992138,0.00001592171,0.0002323143,0.00009422383,0.0002897631,0.0001540123],"domain_scores_gemma":[0.9994333,0.0001343531,0.00007823184,0.0002284108,0.00009252904,0.00003317016],"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":[0.0001222413,0.0007280889,0.001523591,0.001592963,0.000292087,0.00004013893,0.003677777,0.2008301,0.206365,0.01212343,0.01527247,0.5574321],"study_design_scores_gemma":[0.0004174211,0.00002088323,0.0004697107,0.0001834553,0.00005294684,0.0000329588,0.0000912348,0.4044832,0.5879065,0.00508079,0.001063163,0.0001977535],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002082026,0.00027531,0.9959782,0.0003675446,0.000118898,0.0001312635,0.00001260564,0.0001050653,0.0009290286],"genre_scores_gemma":[0.9857013,0.0002675951,0.01347158,0.0001650279,0.0002908844,0.00003255032,0.00001432692,0.0000337976,0.00002299124],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9836192,"threshold_uncertainty_score":0.2954124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005851558217178072,"score_gpt":0.2251841765476518,"score_spread":0.2193326183304738,"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."}}