{"id":"W1981787495","doi":"10.1109/tbme.2013.2262658","title":"Fusion of Electromagnetic Trackers to Improve Needle Deflection Estimation: Simulation Study","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Deflection (physics); Kalman filter; Kinematics; Acoustics; Computer science; Physics; Artificial intelligence; Optics","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":[0.00006482263,0.000158204,0.0001596932,0.0003077371,0.00005855542,0.00002466436,0.00009547472,0.00008781218,0.0001054243],"category_scores_gemma":[0.000009472164,0.0001661958,0.0000569964,0.0006992549,0.00001520901,0.0001053383,9.866651e-7,0.0001837452,0.00007812768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008503023,"about_ca_system_score_gemma":0.00001079297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004405999,"about_ca_topic_score_gemma":0.000002902723,"domain_scores_codex":[0.9990443,0.000007163092,0.0003204605,0.000175742,0.000238105,0.000214249],"domain_scores_gemma":[0.9994606,0.000100869,0.00002052561,0.0002016831,0.00005064899,0.0001656158],"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":[0.000002307851,0.0001638191,0.000001817343,0.00002236248,0.00002311796,2.246487e-7,0.0001594078,0.8399552,0.1334041,0.000005165587,0.00002844995,0.02623395],"study_design_scores_gemma":[0.0003073103,0.0003630412,0.0008996698,0.00002026401,0.00002981247,0.000001419323,0.00005561439,0.9787598,0.01930411,0.00001698199,0.00008487768,0.0001570466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3463746,0.000006548518,0.6525292,0.00004573239,0.00029987,0.0004688651,0.000003790837,0.0002447975,0.00002653742],"genre_scores_gemma":[0.9911464,0.000005132548,0.008471391,0.00001211497,0.00004324614,0.000257241,0.000003373129,0.0000360605,0.0000250694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6447718,"threshold_uncertainty_score":0.6777268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006518841728877339,"score_gpt":0.2272259977033662,"score_spread":0.2207071559744889,"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."}}