{"id":"W4417057525","doi":"10.2139/ssrn.5845386","title":"Craniofacial CBCT: Addressing Volume-Resolution Dilemma using Generative Artificial Intelligence","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Shriners Hospitals for Children - Canada","funders":"","keywords":"Cadaveric spasm; Segmentation; Craniofacial; Image segmentation; Pattern recognition (psychology); Texture (cosmology); Generalization; Active appearance model","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":[0.00137925,0.0005117467,0.0007902794,0.0008697577,0.0003854072,0.002173746,0.001671727,0.001673051,0.00253729],"category_scores_gemma":[0.006125402,0.0005823291,0.0008763103,0.0007971879,0.001138323,0.001203634,0.001475155,0.001453389,0.0003907517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008063763,"about_ca_system_score_gemma":0.0008083262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004831526,"about_ca_topic_score_gemma":0.005117328,"domain_scores_codex":[0.9993789,0.0002104299,0.00002482559,0.0001004633,0.0002493326,0.00003603539],"domain_scores_gemma":[0.9976548,0.001747702,0.0001043388,0.0002794063,0.0001644233,0.00004924244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000300324,0.00008551192,0.003495674,0.0004803759,0.0001497781,0.0007706484,0.0006621371,0.4539807,0.03095172,0.06241849,0.005529267,0.4411753],"study_design_scores_gemma":[0.00001236842,0.00002104045,0.0004782773,0.00002638402,0.00002697018,0.0003496665,0.00005368472,0.9651399,0.004465382,0.02764184,0.001767382,0.00001707545],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0228114,0.001049006,0.9691841,0.001987913,0.00009719588,0.00006477767,0.0001187629,0.0008176644,0.00386919],"genre_scores_gemma":[0.5580589,0.001144686,0.4365195,0.0006132529,0.0002073041,0.00007380826,0.0001961247,0.0004184248,0.00276796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004831526,"threshold_uncertainty_score":0.009606779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567652419699798,"score_gpt":0.3240533756918741,"score_spread":0.2783768514948762,"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."}}