{"id":"W2093671867","doi":"10.1115/imece2006-15763","title":"Integration of Vision and Inertial Sensors for a Surgical Tool Tracking","year":2006,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inertial measurement unit; Kalman filter; Computer science; Computer vision; Artificial intelligence; Orientation (vector space); Tracking system; Calibration; Reliability (semiconductor); Tracking (education); Machine vision; Match moving; Position (finance); Motion (physics)","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":[0.0002577136,0.0003100235,0.000273647,0.0002893407,0.0001419628,0.000396821,0.0003924101,0.0005125399,0.0007722423],"category_scores_gemma":[0.0006908607,0.0002803956,0.0003028966,0.0002229134,0.0001531313,0.0006120143,0.0004098981,0.0003374881,0.0003646803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002221305,"about_ca_system_score_gemma":0.000427943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001185366,"about_ca_topic_score_gemma":0.001771831,"domain_scores_codex":[0.9997024,0.00004100509,0.0000150366,0.00005465903,0.0001648818,0.00002190605],"domain_scores_gemma":[0.9998376,0.00004253813,0.00002402772,0.00002414846,0.00006088601,0.00001061277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003040347,0.0001300113,0.00316593,0.0001723863,0.0001080048,0.00027277,0.0001350968,0.0920831,0.2705297,0.009127188,0.002129691,0.6218421],"study_design_scores_gemma":[0.00002952676,0.0005807321,0.004963377,0.00004459739,0.0001033546,0.0005248028,0.00003298098,0.8890661,0.0818553,0.00308739,0.0196543,0.00005745099],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02332922,0.0007073167,0.9729843,0.0001558808,0.0001096663,0.00002260184,0.00002468079,0.0007419299,0.001924364],"genre_scores_gemma":[0.6535996,0.0008308952,0.3408827,0.0001673278,0.00008503158,0.00005579379,0.00009876505,0.00004360531,0.004236359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001185366,"threshold_uncertainty_score":0.002583385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564784140398367,"score_gpt":0.2860037843179932,"score_spread":0.2703559429140096,"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."}}