{"id":"W3174203047","doi":"10.15607/rss.2021.xvii.029","title":"Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Odometry; Probabilistic logic; Pattern recognition (psychology); Feature (linguistics); Radar; Unsupervised learning; Mobile robot; Robot","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":[0.00008461451,0.00009040673,0.0001228142,0.00004819619,0.0001028994,0.00002051033,0.00004270094,0.0001450675,0.00007637899],"category_scores_gemma":[0.00009861265,0.00009195247,0.00001785717,0.0001974059,0.00003832202,0.00007861475,0.00003304817,0.0003237234,0.00001703203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002643442,"about_ca_system_score_gemma":0.00001366448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001198609,"about_ca_topic_score_gemma":0.000003208758,"domain_scores_codex":[0.9995677,0.00001835566,0.0000945287,0.0001305207,0.00005104329,0.0001378231],"domain_scores_gemma":[0.9997562,0.00007075017,0.00001019308,0.0001094295,0.00002116976,0.00003222337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002842146,0.000115234,0.01988308,0.000954806,0.0003804986,0.0002192242,0.00181531,0.2387971,0.04436744,0.0920167,0.001855123,0.5995671],"study_design_scores_gemma":[0.0006331409,0.00003821036,0.01122583,0.00004410253,0.00003456592,0.0001010093,0.0003742834,0.9730758,0.008460466,0.003477242,0.002249444,0.0002859172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9376386,0.001460237,0.04668776,0.0006151616,0.0001179442,0.0001112778,0.00000138022,0.001831416,0.01153619],"genre_scores_gemma":[0.9820917,0.00004281506,0.01724618,0.00003213087,0.000009027242,0.000004458373,0.00001650617,0.00001600685,0.0005411499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7342787,"threshold_uncertainty_score":0.3749713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005271317232354826,"score_gpt":0.1915073177536094,"score_spread":0.1862360005212546,"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."}}