{"id":"W2799773136","doi":"10.1007/s11548-018-1767-x","title":"Fiducial-based fusion of 3D dental models with magnetic resonance imaging","year":2018,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Fiducial marker; Voxel; Magnetic resonance imaging; Computer science; Computer vision; Radiation treatment planning; Medical imaging; Artificial intelligence; Nuclear medicine; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004035753,0.0001313635,0.0003327822,0.0004975176,0.00006648867,0.00004540054,0.0002437518,0.00005422319,0.00007457279],"category_scores_gemma":[0.00002222712,0.0001070889,0.0001601949,0.0001447484,0.0004209522,0.0003128494,0.00004458244,0.0001739918,0.000002246237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002944444,"about_ca_system_score_gemma":0.00008013371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001358831,"about_ca_topic_score_gemma":0.000007374642,"domain_scores_codex":[0.9986556,0.0001423732,0.0005423226,0.0001667529,0.0003346497,0.0001583207],"domain_scores_gemma":[0.9985845,0.0003791841,0.0004106816,0.0001028946,0.0004554982,0.00006721185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001610127,0.0002145799,0.763678,0.00001707854,0.0001933888,0.00168406,0.00015529,0.0001907803,0.001549097,0.0002452658,0.005322922,0.2251394],"study_design_scores_gemma":[0.001874261,0.0002798777,0.9392421,0.0005407247,0.00006793072,0.01758246,0.00005459696,0.03670146,0.001067952,0.0004142787,0.001941849,0.000232454],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.934227,0.003198602,0.05913123,0.0001947866,0.002833173,0.00003851973,0.00001217229,0.00001276706,0.0003517894],"genre_scores_gemma":[0.9915295,0.00006393384,0.007244962,0.0004593297,0.0006665224,8.512381e-7,0.000007247322,0.00001101845,0.00001661217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2249069,"threshold_uncertainty_score":0.436696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228997304244717,"score_gpt":0.2491969046827174,"score_spread":0.2369069316402702,"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."}}