{"id":"W2113448190","doi":"10.1109/iembs.2009.5334718","title":"Long term three dimensional tracking of orthodontic patients using registered cone beam CT and photogrammetry","year":2009,"lang":"en","type":"article","venue":"","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Photogrammetry; Cone beam ct; Term (time); Tracking (education); Cone (formal languages); Computer science; Cone beam computed tomography; Computer vision; Orthodontics; Artificial intelligence; Medicine; Computed tomography; Radiology; Physics; Psychology; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004883317,0.000259935,0.0004458041,0.001275751,0.0002769819,0.0006005014,0.0003966987,0.0005512331,0.00122262],"category_scores_gemma":[0.001447638,0.0003470239,0.0002819066,0.001411652,0.0002310859,0.0003418095,0.0004536395,0.0004724005,0.0006953073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000309351,"about_ca_system_score_gemma":0.0003625547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005257215,"about_ca_topic_score_gemma":0.0143592,"domain_scores_codex":[0.9994956,0.00006907685,0.000042656,0.0001112786,0.000255139,0.00002622124],"domain_scores_gemma":[0.9993814,0.000110225,0.0001714631,0.0001255898,0.0001727588,0.00003851282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00218433,0.0005215088,0.3154657,0.0002339969,0.0001404831,0.001395982,0.00222861,0.009804164,0.1370319,0.0004711181,0.001941482,0.5285808],"study_design_scores_gemma":[0.00006706813,0.0009464597,0.9090639,0.00004766495,0.000162644,0.004122761,0.000933202,0.04188354,0.03822086,0.0003010829,0.004114864,0.0001359566],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700866,0.000485151,0.0256443,0.00008355756,0.00002026021,0.0001350916,0.001081405,0.000369225,0.002094303],"genre_scores_gemma":[0.9599513,0.0004164294,0.03623928,0.00003810462,0.00001981961,0.0001353856,0.001030388,0.00007003114,0.002099271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005257215,"threshold_uncertainty_score":0.01045322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03455933804374407,"score_gpt":0.2881152035691901,"score_spread":0.253555865525446,"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."}}