{"id":"W2901602847","doi":"10.1002/cre2.141","title":"Optimization of cone beam computed tomography image quality in implant dentistry","year":2018,"lang":"en","type":"article","venue":"Clinical and Experimental Dental Research","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cone beam computed tomography; Computed tomography; Implant; Cone beam ct; Image quality; Dentistry; Tomography; Cone (formal languages); Medicine; Orthodontics; Image (mathematics); Computer science; Radiology; Computer vision; Surgery","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":[0.001889285,0.0001753981,0.0004229737,0.0003773369,0.0001994554,0.0001250394,0.0003416097,0.0001577206,0.000378152],"category_scores_gemma":[0.0001571129,0.0001707657,0.0002104199,0.0008524444,0.002702868,0.000334805,0.0004945897,0.0004711382,0.00009647186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000335562,"about_ca_system_score_gemma":0.0000270833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005979823,"about_ca_topic_score_gemma":0.0000717389,"domain_scores_codex":[0.9967471,0.000518278,0.0009865396,0.000565363,0.0006943622,0.0004882918],"domain_scores_gemma":[0.9987203,0.0004215213,0.0001269485,0.000310021,0.0001585913,0.0002625446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001380804,0.002508233,0.8988856,0.0001047122,0.0001068172,0.0004492433,0.0002748719,0.000005641105,0.09084423,0.0005304451,0.002280445,0.002628949],"study_design_scores_gemma":[0.004265219,0.0009492631,0.7803656,0.000185018,0.00001111127,0.0003775025,0.003583169,0.001606712,0.2079068,0.0002543801,0.0001612592,0.0003340038],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953105,0.0007863936,0.0003011294,0.00001685331,0.0009412183,0.0003098368,0.00007525855,0.0000335824,0.002225188],"genre_scores_gemma":[0.9981446,0.00005209503,0.001331895,0.00006839041,0.0001805959,0.00001826816,0.00006572904,0.00001981065,0.000118607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.11852,"threshold_uncertainty_score":0.995883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1092175001049489,"score_gpt":0.4842835024594163,"score_spread":0.3750660023544675,"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."}}