{"id":"W1548672224","doi":"10.1109/robot.1999.770359","title":"On inverse kinematics and trajectory planning for tele-laparoscopic manipulation","year":2003,"lang":"en","type":"article","venue":"","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Intelligent Systems Center","keywords":"Inverse kinematics; Kinematics; Trajectory; Computer science; Rotation (mathematics); Manipulator (device); Kinematics equations; Control theory (sociology); Simulation; Robot kinematics; Computer vision; Artificial intelligence; Robot; Physics; Mobile robot; Control (management)","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":[0.00002845003,0.000051326,0.00005356358,0.00002758605,0.00003405645,0.0000126559,0.0000166338,0.00002727594,0.00001598317],"category_scores_gemma":[0.00001727266,0.00004974123,0.00001026198,0.0000365985,0.000005362414,0.00002049478,0.000001403829,0.00002844935,0.000005279908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001088626,"about_ca_system_score_gemma":0.000002407933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.244873e-7,"about_ca_topic_score_gemma":0.000001964547,"domain_scores_codex":[0.9997687,0.000002056906,0.00007283227,0.00005660363,0.00002934328,0.00007044646],"domain_scores_gemma":[0.9998258,0.00006894004,0.000007525447,0.00006572124,0.000006711468,0.00002526668],"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":[0.000001325702,0.00001735749,0.000988413,0.0001079001,0.00001481363,2.324977e-7,0.0002027508,0.8691679,0.003369721,0.1214985,0.004361333,0.0002697917],"study_design_scores_gemma":[0.000939039,0.00006427476,0.004688386,0.00005180269,0.00003198135,0.00000315939,0.0002221795,0.965396,0.01162286,0.01237363,0.004301358,0.0003053452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7383393,0.00003618058,0.2501769,0.00001639994,0.00009901593,0.0002577587,0.000001881801,0.0001553967,0.01091714],"genre_scores_gemma":[0.9666769,0.000003655469,0.03305132,0.00004760405,0.00001606392,0.00003346837,0.000005309613,0.00001336554,0.0001523026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2283376,"threshold_uncertainty_score":0.2028388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03221658084494475,"score_gpt":0.2557842612982665,"score_spread":0.2235676804533217,"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."}}