{"id":"W3014129310","doi":"10.1007/978-3-030-43195-2_50","title":"Creation of Categorical Mandible Atlas to Benefit Non-Rigid Registration","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computational vision and biomechanics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Hausdorff distance; Artificial intelligence; Categorical variable; Orthodontics; Mathematics; Polygon mesh; Image registration; Computer science; Mandible (arthropod mouthpart); Medicine; Pattern recognition (psychology); Computer vision; Statistics; Image (mathematics); Geometry; Biology","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":[0.00008626819,0.0002131899,0.0003349694,0.0003008099,0.00002902012,0.00002973295,0.00008476507,0.0002346353,0.00006619572],"category_scores_gemma":[0.00004258726,0.0001903083,0.00007406878,0.0001809031,0.00002176266,0.00003321177,0.00003418554,0.0002385533,0.00001940702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000537024,"about_ca_system_score_gemma":0.00002543002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001304818,"about_ca_topic_score_gemma":0.00001166168,"domain_scores_codex":[0.9989339,0.000005599976,0.0003731406,0.0002363649,0.0003474925,0.0001035731],"domain_scores_gemma":[0.9994869,0.0001484315,0.00007626951,0.00009593611,0.00007874562,0.0001137538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004628484,0.00005117619,0.00001483467,0.0007116807,0.0001879543,0.00003765461,0.0004068918,0.4537367,0.007006541,0.06455871,0.001766052,0.4714756],"study_design_scores_gemma":[0.000212019,0.00009901739,0.00002878824,0.0002023235,0.00004453932,0.000006389885,0.000001969,0.8275797,0.000967628,0.1670002,0.003622946,0.0002344293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004137621,0.0003848673,0.9936207,0.001398609,0.0001557716,0.0001566699,0.00003512691,0.00006161946,0.003772889],"genre_scores_gemma":[0.9856064,0.0002542658,0.01213097,0.0003666287,0.0001814641,0.000004369203,0.0006721449,0.00004506773,0.0007387389],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9851926,"threshold_uncertainty_score":0.7760546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069151918896137,"score_gpt":0.2498347686191686,"score_spread":0.2391432494302072,"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."}}