{"id":"W4385238018","doi":"10.1177/02783649231183460","title":"GROUNDED: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather","year":2023,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; Lincoln Laboratory, Massachusetts Institute of Technology","keywords":"Ground truth; Odometry; Lidar; Computer science; Artificial intelligence; Global Positioning System; Radar; Ground-penetrating radar; Visual odometry; Computer vision; GNSS applications; Remote sensing; Inertial measurement unit; Robot; Geography; Mobile 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":[],"consensus_categories":[],"category_scores_codex":[0.006258955,0.00007225502,0.0001134117,0.000316636,0.0001079336,0.0001388281,0.0005794045,0.00002852594,0.0000195657],"category_scores_gemma":[0.000472041,0.00005698785,0.00004463506,0.0004951831,0.000030204,0.0001065615,0.0001388468,0.0004155114,0.00003304529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002884661,"about_ca_system_score_gemma":0.0000648947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007749605,"about_ca_topic_score_gemma":0.00006375164,"domain_scores_codex":[0.9979668,0.0001858663,0.000359674,0.00009940444,0.001144835,0.0002433738],"domain_scores_gemma":[0.9982266,0.000991978,0.00005509949,0.0001172234,0.0005452118,0.0000638496],"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":[0.00004126563,0.00004819472,0.00008559605,0.00002072129,0.00007777955,0.000006472178,0.0008155733,0.9247628,0.01727057,0.003506146,0.003059636,0.05030518],"study_design_scores_gemma":[0.001351833,0.0002312266,0.006496607,0.0002573341,0.00002604391,0.00001944165,0.002926576,0.9172487,0.0008321806,0.0458925,0.02452769,0.0001898702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5707569,0.0002376047,0.4117055,0.01401547,0.0008435696,0.001591125,0.0001325078,0.00005862875,0.0006587211],"genre_scores_gemma":[0.9784984,0.00006355646,0.0207035,0.00006195554,0.000420834,0.00006921938,0.00009881933,0.00002624889,0.00005745918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4077415,"threshold_uncertainty_score":0.2323897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1881420423010262,"score_gpt":0.4727939130794336,"score_spread":0.2846518707784074,"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."}}