{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001427338,0.0001635357,0.0002680767,0.0001869941,0.00009960641,0.00007482662,0.0001082114,0.00003154798,0.00007580929],"category_scores_gemma":[0.00004010525,0.0001515823,0.0001375762,0.0002712611,0.000106692,0.0003059737,0.00004538575,0.0001199324,0.000003384451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001847364,"about_ca_system_score_gemma":0.00000948544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002721704,"about_ca_topic_score_gemma":0.0001366645,"domain_scores_codex":[0.9987562,0.00002679314,0.0003746384,0.0002876403,0.000303464,0.0002512342],"domain_scores_gemma":[0.9993614,0.00005275797,0.0001832308,0.00024253,0.0000688643,0.00009119175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004507827,0.0001943752,0.9544035,0.00003651468,0.00004464199,0.00009336341,0.000019869,0.000002991962,0.00837732,0.00002163455,0.00004122512,0.03671955],"study_design_scores_gemma":[0.001178064,0.00007398378,0.9890803,0.0001481814,0.00006808634,0.000202504,0.00002182579,0.000495745,0.008438277,0.0001076746,0.000007181054,0.0001781398],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961562,0.0003925819,0.002193799,0.000006019095,0.0002581179,0.0001939444,0.00001011197,0.00004351395,0.0007456853],"genre_scores_gemma":[0.9980847,0.000004050851,0.00164355,0.0001352458,0.00002510227,7.529836e-7,0.000019207,0.00001389511,0.00007348527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03654141,"threshold_uncertainty_score":0.6181348,"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."}}