{"id":"W7128499471","doi":"10.56578/jisc040203","title":"Bayesian Estimation of Hand Kinematics from Spatially Tracked Landmarks","year":2025,"lang":"","type":"article","venue":"Journal of Intelligent Systems and Control","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Bayesian probability; Bayes estimator; Pattern recognition (psychology); Estimation","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":[0.001060664,0.0007027525,0.0008372786,0.0009887273,0.0002731684,0.000787455,0.001171514,0.0008568518,0.0008608338],"category_scores_gemma":[0.004809536,0.0009053879,0.000672681,0.0009587444,0.0007711315,0.001091057,0.001090218,0.001017212,0.0004385499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005948251,"about_ca_system_score_gemma":0.001586044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009417383,"about_ca_topic_score_gemma":0.01197277,"domain_scores_codex":[0.999341,0.0001625029,0.00003597644,0.0002187473,0.0001822876,0.00005953518],"domain_scores_gemma":[0.9990885,0.0004299856,0.0001640975,0.0001106434,0.0001718956,0.00003492064],"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":[0.0001109699,0.00003699902,0.001987121,0.0001096622,0.00005327924,0.00008032858,0.00009144105,0.8373366,0.01093691,0.01517784,0.0006922311,0.1333866],"study_design_scores_gemma":[0.000006115333,0.00002841623,0.00085242,0.00001611378,0.000008598739,0.00004786926,0.000009902782,0.9894283,0.001612082,0.007259115,0.0007125727,0.00001842953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003907262,0.00009748513,0.9956082,0.0000192758,0.000005418498,0.00001078004,0.00004054193,0.0001266297,0.0001843168],"genre_scores_gemma":[0.4787375,0.0008013596,0.5169736,0.00007736379,0.0000602064,0.0001925622,0.0006620659,0.0001202443,0.002375119],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009417383,"threshold_uncertainty_score":0.01872516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00950208287014086,"score_gpt":0.2385211849894075,"score_spread":0.2290191021192667,"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."}}