{"id":"W1526676349","doi":"10.1007/978-3-540-39899-8_5","title":"Needle Steering and Model-Based Trajectory Planning","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Jacobian matrix and determinant; Computer science; Deflection (physics); Obstacle avoidance; Motion planning; Trajectory; Simulation; Robot; Artificial intelligence; Mobile robot; Physics; Mathematics; Optics; Applied mathematics","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":[0.0008308982,0.0005802407,0.0005482941,0.0008928312,0.0002801711,0.0005158429,0.002016122,0.0003298794,0.000002502335],"category_scores_gemma":[0.00007263495,0.000577098,0.00008483723,0.000432995,0.0004996154,0.0004805292,0.0005446424,0.0009083429,0.000008023004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002441139,"about_ca_system_score_gemma":0.0005347769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006453407,"about_ca_topic_score_gemma":0.000001637633,"domain_scores_codex":[0.9963339,0.00003417696,0.0004439963,0.001566049,0.0008597519,0.000762154],"domain_scores_gemma":[0.9978805,0.0004131677,0.0002206534,0.001124098,0.0001151973,0.0002464343],"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":[0.00000161384,0.00001011042,0.00005144126,0.00003476806,0.00000519756,0.000116205,0.0006095825,0.9135643,0.00008277672,0.001254583,0.00001456509,0.08425491],"study_design_scores_gemma":[0.0002590652,0.00009564391,0.00007376466,0.0005603252,0.000006101176,0.00007609064,1.579506e-7,0.9834433,0.0003348383,0.01438362,0.0001355042,0.0006315837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001042543,0.0009234168,0.9948355,0.0002349849,0.001166599,0.000251892,0.000003050298,0.000225694,0.002254628],"genre_scores_gemma":[0.02424159,0.00000733402,0.9736492,0.00170752,0.0001485699,0.000007009417,0.000002353392,0.00004344966,0.0001929439],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08362332,"threshold_uncertainty_score":0.9996681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02569298469996744,"score_gpt":0.2467110390139329,"score_spread":0.2210180543139654,"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."}}