{"id":"W3012888745","doi":"10.1109/lra.2020.3007381","title":"Variational Inference With Parameter Learning Applied to Vehicle Trajectory Estimation","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; Trajectory; Outlier; Context (archaeology); Artificial intelligence; Machine learning; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008976807,0.0001056136,0.0001070233,0.0000524065,0.0001443049,0.0002404721,0.0001688527,0.00003521029,0.000006181589],"category_scores_gemma":[0.00004060032,0.0000954214,0.00001411651,0.0002524737,0.00002012358,0.000267557,0.00003725019,0.0001393737,0.00003355409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001356896,"about_ca_system_score_gemma":0.00001765986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003188175,"about_ca_topic_score_gemma":5.892273e-7,"domain_scores_codex":[0.9991429,0.00003543777,0.0001650015,0.0002789029,0.0002306632,0.0001471259],"domain_scores_gemma":[0.9994676,0.0001810691,0.00007806372,0.0001268545,0.00003204846,0.0001143317],"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":[0.000005446945,0.000007603649,0.0003453786,0.000009503031,0.000006911582,0.000002058519,0.0007283715,0.9799509,0.003501963,0.004329806,0.0006272067,0.01048487],"study_design_scores_gemma":[0.0002068538,0.00005109295,0.00785976,0.00001575864,0.00000568921,0.000002192387,0.000008577088,0.9911178,0.0002921736,0.00008340709,0.0002137037,0.0001429372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06630141,0.000003020683,0.9257395,0.00746481,0.0001005921,0.0001067734,0.000001287484,0.0002416577,0.0000408958],"genre_scores_gemma":[0.6927553,9.477749e-7,0.3030424,0.004124817,0.00005218044,0.000006290288,0.000009682706,0.000005851452,0.000002536413],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6264538,"threshold_uncertainty_score":0.3891172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01596787004869859,"score_gpt":0.2229417200090569,"score_spread":0.2069738499603583,"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."}}