{"id":"W1983852298","doi":"10.1109/ccece.2006.277568","title":"Predictive Teleoperation using Laser Rangefinder","year":2006,"lang":"en","type":"article","venue":"","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Teleoperation; Control theory (sociology); Synchronization (alternating current); Controller (irrigation); Telerobotics; Master/slave; Model predictive control; Position (finance); Computer science; Stability (learning theory); Haptic technology; Control engineering; Collision avoidance; Collision; Simulation; Engineering; Robot; Mobile robot; Control (management); Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002510534,0.0002721012,0.0002679231,0.0002193594,0.0002551402,0.0003417954,0.0006754333,0.0003604507,0.0006991516],"category_scores_gemma":[0.0007437447,0.0001613571,0.000132905,0.0001804079,0.0003349923,0.0005509356,0.0004386272,0.0004435242,0.0001184915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002629885,"about_ca_system_score_gemma":0.0003305943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001983393,"about_ca_topic_score_gemma":0.001772047,"domain_scores_codex":[0.9997471,0.0000301278,0.000006111904,0.00004678647,0.0001529819,0.00001683871],"domain_scores_gemma":[0.9997258,0.000129783,0.00005324542,0.00004910042,0.00003244594,0.000009627235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005269983,0.0001629352,0.001534091,0.0001695803,0.00003823325,0.0004696997,0.0003527078,0.3081246,0.2712508,0.008924042,0.001577352,0.4068691],"study_design_scores_gemma":[0.00004774098,0.000257846,0.0009141864,0.00001134333,0.00001555069,0.0002417582,0.00003068276,0.9504377,0.04444866,0.001819384,0.001755221,0.00001996882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1441662,0.0005880302,0.8488485,0.00020068,0.00006118113,0.00004622684,0.00001969379,0.00200701,0.004062557],"genre_scores_gemma":[0.9648057,0.0001381464,0.03368281,0.00002409556,0.00001494752,0.00002624764,0.00001040653,0.00001135423,0.001286357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001983393,"threshold_uncertainty_score":0.003943741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00943004708348904,"score_gpt":0.192409157108537,"score_spread":0.1829791100250479,"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."}}