{"id":"W4287604507","doi":"10.48550/arxiv.2011.03512","title":"Do We Need to Compensate for Motion Distortion and Doppler Effects in\\n Spinning Radar Navigation?","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies","funders":"","keywords":"Odometry; Radar; Computer science; Distortion (music); Computer vision; Radar imaging; Artificial intelligence; Doppler radar; Spinning; Radar engineering details; Remote sensing; Geography; Engineering; Telecommunications","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":[0.00007906619,0.0002031003,0.0002431368,0.0001817037,0.00005889838,0.00004861629,0.0001086131,0.0001705892,0.000002594095],"category_scores_gemma":[0.00002209364,0.0002654672,0.00006139979,0.0002778946,0.00001902204,0.00008748713,0.00009101014,0.0002180937,0.000007287613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002242043,"about_ca_system_score_gemma":0.00001410859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004502253,"about_ca_topic_score_gemma":0.00001548106,"domain_scores_codex":[0.9991773,0.00003393126,0.0001634546,0.0004069831,0.00004843179,0.0001698445],"domain_scores_gemma":[0.9995285,0.00005416797,0.00005748624,0.0001907386,0.00005522428,0.0001138648],"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":[0.00003062856,0.000009671629,0.001674465,0.0005732334,0.0000264628,0.00002405625,0.0002900584,0.9877389,0.0006630334,0.008309517,0.00008539853,0.0005745716],"study_design_scores_gemma":[0.0006280349,0.00004116475,0.003762751,0.0003569857,0.00006063534,8.062833e-7,0.00008147979,0.9868881,0.0004512898,0.007260015,0.0001631191,0.0003056189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.416014,0.00005040837,0.5829711,0.00009629114,0.0002520045,0.000469365,0.00001140489,0.0001002479,0.00003524849],"genre_scores_gemma":[0.9980814,0.00009960132,0.001519968,0.00002732618,0.00006362102,0.000002714357,0.0001496621,0.00003621738,0.0000195167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5820674,"threshold_uncertainty_score":0.9999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04260156722942778,"score_gpt":0.1856373739836086,"score_spread":0.1430358067541808,"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."}}