{"id":"W2126153312","doi":"10.1109/iembs.2004.1403782","title":"Trajectory generation for robotic needle insertion in soft tissue","year":2005,"lang":"en","type":"article","venue":"","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Deformation (meteorology); Trajectory; Displacement (psychology); Soft tissue; Brachytherapy; Robot; Computer science; Biomedical engineering; Prostate brachytherapy; Percutaneous; Computer vision; Artificial intelligence; Materials science; Surgery; Physics; Engineering; Medicine; Radiation therapy","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":[0.00003484457,0.00004817705,0.00005088249,0.00003424157,0.00002498302,0.00001365907,0.00003364547,0.00003667377,0.00007048434],"category_scores_gemma":[0.000004578504,0.00005310699,0.00001357349,0.00006086422,0.000003784605,0.00006018526,0.000002378789,0.00003115601,0.00006561369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000362945,"about_ca_system_score_gemma":0.000004917445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001379274,"about_ca_topic_score_gemma":0.0005133306,"domain_scores_codex":[0.9997116,0.00000178927,0.00009457795,0.00007023049,0.00003033078,0.00009144166],"domain_scores_gemma":[0.999872,0.00001425745,0.000005594244,0.00007634976,0.0000105799,0.00002119227],"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":[3.266071e-7,0.00001381948,0.00004852537,0.000005812805,0.00000181885,2.893584e-8,0.00008390596,0.9054369,0.07710367,0.0005564606,0.002755707,0.01399307],"study_design_scores_gemma":[0.0001494116,0.000007638489,0.001299608,0.00000213623,0.000003179381,4.690291e-7,0.00001529511,0.9825747,0.01070031,0.0001146318,0.005058858,0.00007377024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1068573,0.0001643527,0.8912079,0.0003230964,0.00008655393,0.0002459027,4.961424e-7,0.0001654212,0.0009489482],"genre_scores_gemma":[0.9767345,0.00001254599,0.0225646,0.00006548281,0.0001977394,0.0000646597,0.00002405192,0.00001384856,0.000322551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8698772,"threshold_uncertainty_score":0.216564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02411251214346135,"score_gpt":0.245711871869064,"score_spread":0.2215993597256026,"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."}}