{"id":"W3207562721","doi":"10.48550/arxiv.1710.11319","title":"Learning Motion Predictors for Smart Wheelchair using Autoregressive\\n Sparse Gaussian Process","year":2017,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Joystick; Artificial intelligence; Wheelchair; Black box; Process (computing); Computer science; Motion control; Simulation; Motion capture; Engineering; Motion (physics); Computer vision; Control engineering; Robot","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":[0.0004352829,0.0005688388,0.0005969608,0.0003826746,0.0002625097,0.0003277996,0.0005897625,0.0005223682,0.001375437],"category_scores_gemma":[0.001350882,0.0003524141,0.0004216766,0.0004300601,0.000227478,0.0004375521,0.0004505691,0.0008673956,0.0005576719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004223502,"about_ca_system_score_gemma":0.0008791382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02000285,"about_ca_topic_score_gemma":0.02200233,"domain_scores_codex":[0.9998543,0.00002491983,0.000006931175,0.00005153026,0.00003481024,0.00002748857],"domain_scores_gemma":[0.9997279,0.00010698,0.00003984553,0.00002220853,0.0000855229,0.00001750985],"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":[0.0003294758,0.0002026458,0.002685171,0.00009804024,0.00009707173,0.000107093,0.0001243799,0.6287695,0.01419685,0.001969195,0.003529711,0.3478909],"study_design_scores_gemma":[0.000004181407,0.00001939659,0.0002828834,0.000002807087,0.000004812054,0.000004659726,0.00000362686,0.9986336,0.0006555696,0.0002222752,0.000163778,0.000002457963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06512845,0.000356403,0.9300718,0.0002293434,0.00006319052,0.00005493895,0.0001482954,0.002750291,0.001197253],"genre_scores_gemma":[0.8888516,0.0003385342,0.1057236,0.0001404218,0.00005941764,0.0001206619,0.0004919572,0.00008552526,0.004188235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02000285,"threshold_uncertainty_score":0.03977281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1033728855283823,"score_gpt":0.2331764931806566,"score_spread":0.1298036076522743,"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."}}