{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002069397,0.0003748726,0.0005199516,0.0003505328,0.0001019143,0.000134436,0.0008647172,0.0002075104,0.00003791659],"category_scores_gemma":[0.00004301111,0.0003124175,0.00003733106,0.0001913664,0.0009469769,0.0003088057,0.0003816507,0.0007513372,0.000006430997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009875947,"about_ca_system_score_gemma":0.0006299464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007855868,"about_ca_topic_score_gemma":0.0022522,"domain_scores_codex":[0.9976365,0.00001466311,0.0003168912,0.001212469,0.0004789839,0.0003404688],"domain_scores_gemma":[0.9981595,0.0001141577,0.0001110301,0.001339099,0.0001190169,0.0001571879],"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":[0.0000779058,0.0001037107,0.003593857,0.00007888069,0.00006082247,0.0005050319,0.0002870251,0.01726375,0.001558504,0.0002372227,0.000366417,0.9758669],"study_design_scores_gemma":[0.0006279543,0.0001165179,0.000829211,0.0003149762,0.00005779896,0.0001021571,0.000001118661,0.9829468,0.00006417419,0.01419019,0.0004152826,0.000333836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02144729,0.0006084942,0.9749781,0.0004184148,0.0002821598,0.0003530282,0.0003213181,0.0001043484,0.001486824],"genre_scores_gemma":[0.7309245,0.00009324414,0.2644413,0.001889482,0.000150435,0.000002025204,0.002148994,0.00005539231,0.0002945936],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9755331,"threshold_uncertainty_score":0.9999328,"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."}}