{"id":"W2789767748","doi":"10.1142/s2424905x18420047","title":"Event-Triggered 3D Needle Control Using a Reduced-Order Computationally Efficient Bicycle Model in a Constrained Optimization Framework","year":2018,"lang":"en","type":"article","venue":"Journal of Medical Robotics Research","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Nuclear Physics; Natural Sciences and Engineering Research Council of Canada; University of Alberta; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions","keywords":"Deflection (physics); Kinematics; Computer science; Limiting; Brachytherapy; Trajectory; Simulation; Control theory (sociology); Biomedical engineering; Engineering; Artificial intelligence; Surgery; Physics; Mechanical engineering; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002311282,0.0001166423,0.0002759761,0.0004778264,0.0001245877,0.00007626974,0.0003255347,0.0002378541,0.0001213576],"category_scores_gemma":[0.001799679,0.0001071713,0.00006191567,0.001044834,0.0002736732,0.00005847988,0.00005271178,0.001028248,0.000009126692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000224256,"about_ca_system_score_gemma":0.0007745987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007814364,"about_ca_topic_score_gemma":0.000003310871,"domain_scores_codex":[0.9967837,0.0001153899,0.0007058688,0.0001348574,0.001851742,0.0004084174],"domain_scores_gemma":[0.997584,0.0007574768,0.00009801375,0.0001533327,0.001056398,0.0003507988],"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":[0.0000254232,0.000165381,0.00003853276,0.00002000299,0.00003808366,0.00001996474,0.0002337809,0.9961063,0.0003855001,0.002106721,0.0002016747,0.0006586075],"study_design_scores_gemma":[0.001040333,0.00006800799,0.00006985868,0.0002761308,0.00001298318,0.00003825224,0.0000985958,0.9960442,0.00003818414,0.002205919,0.00001083005,0.00009677361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04327229,0.0001373189,0.9537947,0.002221902,0.0001873116,0.000204577,0.000003940926,0.00001861869,0.0001593051],"genre_scores_gemma":[0.7517769,0.00004344,0.2477755,0.00006878227,0.0003007259,0.000003642171,0.000002027247,0.00002339459,0.00000559006],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7085046,"threshold_uncertainty_score":0.4467284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06108438161378112,"score_gpt":0.3964388010259983,"score_spread":0.3353544194122172,"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."}}