{"id":"W4406032241","doi":"10.48550/arxiv.2501.00744","title":"Assessing the Distributional Fidelity of Synthetic Chest X-rays using the Embedded Characteristic Score","year":2025,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; National Human Genome Research Institute; Parker Institute for Cancer Immunotherapy; Canadian Institute for Advanced Research","keywords":"Generative grammar; Image (mathematics); Computer science; Generative model; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.005481796,0.0006657629,0.000439644,0.001428612,0.0003068779,0.001442371,0.0006820565,0.001231347,0.001129801],"category_scores_gemma":[0.04310236,0.0002664405,0.0004703043,0.0008491473,0.001452926,0.00146117,0.001618266,0.001025816,0.0002738256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008781416,"about_ca_system_score_gemma":0.0006138997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913848,"about_ca_topic_score_gemma":0.001549421,"domain_scores_codex":[0.9983578,0.0007332598,0.000117246,0.0002951738,0.0004125488,0.0000840299],"domain_scores_gemma":[0.9774915,0.0160852,0.001779648,0.002695019,0.001505634,0.0004429711],"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":[0.0008022275,0.0001637427,0.05425477,0.000352128,0.0001769829,0.0004153016,0.0002996879,0.8357499,0.01216654,0.01619656,0.002823008,0.07659926],"study_design_scores_gemma":[0.00002968977,0.0002118782,0.01320288,0.00005563139,0.0000256799,0.0006167507,0.0001177521,0.9638461,0.01002916,0.01058305,0.001235448,0.00004599153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7113433,0.0008056152,0.281559,0.0008570079,0.0001154197,0.0001423607,0.001639582,0.0008316019,0.002706208],"genre_scores_gemma":[0.9680477,0.0001955801,0.02965275,0.000082709,0.00003070591,0.00004100347,0.001491296,0.00009578814,0.0003624795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005481796,"threshold_uncertainty_score":0.02899086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09507198635032155,"score_gpt":0.2317631542722889,"score_spread":0.1366911679219673,"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."}}