{"id":"W2891275937","doi":"10.1109/lra.2019.2891492","title":"A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on <italic>SE(3)</italic>","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jerk; Acceleration; White noise; Trajectory; Noise (video); Constant (computer programming); Additive white Gaussian noise; Computer science; Motion (physics); Mathematics; Control theory (sociology); Artificial intelligence; Statistics; Physics; Classical mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007877343,0.0009679304,0.0008658304,0.000682551,0.0004343832,0.00125367,0.001309538,0.001288392,0.007585428],"category_scores_gemma":[0.003840792,0.0006318655,0.0008938509,0.001325648,0.001101686,0.001554393,0.00130745,0.002521337,0.005527616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008754832,"about_ca_system_score_gemma":0.001459352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01086293,"about_ca_topic_score_gemma":0.01241512,"domain_scores_codex":[0.999102,0.0001728745,0.00004500754,0.0003282424,0.0002885339,0.00006334358],"domain_scores_gemma":[0.9990575,0.000300452,0.00009242483,0.0002984219,0.0002115047,0.00003977884],"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":[0.0001438287,0.0000688095,0.0008273233,0.00013741,0.00005149256,0.0001448007,0.00007202899,0.5429087,0.009321623,0.03344403,0.01737315,0.3955069],"study_design_scores_gemma":[0.000007599577,0.00002207756,0.000573518,0.00002404982,0.000005210352,0.0000604286,0.000008463852,0.9793354,0.002906608,0.008963864,0.008077146,0.00001567186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003570418,0.0001795777,0.9928893,0.0001996843,0.000077725,0.00002307677,0.0003020292,0.001234238,0.001523893],"genre_scores_gemma":[0.3322929,0.001010919,0.6370651,0.000609418,0.0004175542,0.0003173077,0.006585673,0.001232917,0.02046815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01086293,"threshold_uncertainty_score":0.02537578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145324200891818,"score_gpt":0.2288304612403704,"score_spread":0.2173772192314523,"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."}}