{"id":"W3171161062","doi":"10.48550/arxiv.2105.14152","title":"Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Odometry; Computer science; Radar; Artificial intelligence; Probabilistic logic; Trajectory; Feature (linguistics); Estimator; Computer vision; Mobile robot; Telecommunications; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001425655,0.0002752511,0.0003425424,0.0002030416,0.0001780414,0.00004801106,0.0002412074,0.0006590235,0.00003268908],"category_scores_gemma":[0.00007377223,0.000356273,0.00008259634,0.0003574181,0.0001295742,0.0001372407,0.0003896329,0.001482701,0.00001087297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001730982,"about_ca_system_score_gemma":0.00005447358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001294657,"about_ca_topic_score_gemma":0.00001074781,"domain_scores_codex":[0.9990009,0.00006677298,0.0001350102,0.0005075596,0.00004254196,0.0002472105],"domain_scores_gemma":[0.9993486,0.00008740109,0.00006560469,0.0003685642,0.00005130962,0.00007847572],"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":[0.00001331074,0.0000214103,0.003234662,0.0004012461,0.000161026,0.0001894669,0.0002642669,0.9864158,0.0001537055,0.006019907,0.00003654814,0.00308867],"study_design_scores_gemma":[0.0004334571,0.0000252875,0.00353116,0.0001701686,0.0001442317,0.00001337141,0.0003471671,0.9905002,0.0001419025,0.004179997,0.000117501,0.0003956294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9246517,0.0005664693,0.07220025,0.000042852,0.0002306507,0.0001973693,0.000005828505,0.001016281,0.001088569],"genre_scores_gemma":[0.9973993,0.0003088044,0.001768481,0.00001079717,0.00001802878,0.000001042561,0.00008574305,0.00003782645,0.0003699319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07274762,"threshold_uncertainty_score":0.9998889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02359011091873136,"score_gpt":0.1577805368175953,"score_spread":0.134190425898864,"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."}}