{"id":"W2138972108","doi":"10.1007/11790273_19","title":"An Integrated Dynamic Jaw and Laryngeal Model Constructed from CT Data","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Context (archaeology); Larynx; Flexibility (engineering); Vocal tract; Set (abstract data type); Computational model; Displacement (psychology); Artificial intelligence; Anatomy; Speech recognition; Medicine; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003348422,0.0005708168,0.0005327053,0.0007718906,0.0002652955,0.001010762,0.001025673,0.001133907,0.005216873],"category_scores_gemma":[0.0008936899,0.0005655923,0.001098988,0.0005865316,0.0002434113,0.0005351296,0.0006704106,0.0005072146,0.002271345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003691477,"about_ca_system_score_gemma":0.001242577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006114612,"about_ca_topic_score_gemma":0.007775976,"domain_scores_codex":[0.99983,0.00001199128,0.00001343374,0.00004034218,0.00009251969,0.00001165528],"domain_scores_gemma":[0.9998789,0.00004263753,0.00001104513,0.00001868407,0.00003874912,0.000009973678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003929174,0.00008791529,0.002642572,0.0003335317,0.0001473477,0.001055906,0.0002060892,0.506347,0.1346616,0.006445305,0.006913992,0.3407658],"study_design_scores_gemma":[0.00002144886,0.00008821742,0.001696516,0.00003031898,0.00009487093,0.0008382801,0.00003452822,0.9695085,0.01697166,0.002033094,0.008627736,0.00005483471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009929787,0.0001075093,0.9843508,0.00008528533,0.00007081983,0.00008444284,0.0007940076,0.003062853,0.001514479],"genre_scores_gemma":[0.300225,0.0007255732,0.6824843,0.000161197,0.00005885906,0.0003351273,0.002949347,0.00128527,0.01177535],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006114612,"threshold_uncertainty_score":0.01745218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914831661675646,"score_gpt":0.2730651312693923,"score_spread":0.2539168146526358,"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."}}