{"id":"W1990633464","doi":"10.1109/crv.2013.37","title":"Blinded by the Light: Exploiting the Deficiencies of a Laser Rangefinder for Rover Attitude Estimation","year":2013,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Lidar; Inclinometer; Traverse; Computer science; Odometry; Orientation (vector space); Remote sensing; Ephemeris; Computer vision; Sky; Artificial intelligence; Geodesy; Satellite; Mobile robot; Geology; Robot; Geography; Engineering; Mathematics; Meteorology; Aerospace engineering","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":[0.0001615988,0.00007632685,0.0000837197,0.0000200927,0.0001160488,0.00001490631,0.0001761089,0.00007616084,0.00008285383],"category_scores_gemma":[0.00003856429,0.00003988383,0.00004160541,0.00008415715,0.00006082957,0.0001083375,0.00002303929,0.00009315732,0.00003392057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001497295,"about_ca_system_score_gemma":0.000006494136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001922602,"about_ca_topic_score_gemma":0.00001497338,"domain_scores_codex":[0.999552,0.000008079606,0.0001539309,0.00007073025,0.00006624546,0.0001489976],"domain_scores_gemma":[0.9996095,0.0001332773,0.00003010765,0.0001838407,0.00003311104,0.00001010568],"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":[0.00006452345,0.0002972698,0.003832591,0.000403118,0.0006491539,8.051771e-7,0.01147443,0.2857986,0.1256616,0.137715,0.1854587,0.2486441],"study_design_scores_gemma":[0.0007977702,0.00005247498,0.004378078,0.0000182679,0.00004558548,0.000005426054,0.001965745,0.7570369,0.2231287,0.006558734,0.005756947,0.0002553859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8361355,0.0002432427,0.1516273,0.003979128,0.00007427464,0.0006160587,0.000005022823,0.0003756369,0.006943893],"genre_scores_gemma":[0.9979368,0.000004668861,0.001464573,0.0001347267,0.0000100617,0.0001370917,0.000001893559,0.00001118432,0.0002989973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4712383,"threshold_uncertainty_score":0.1626415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362245232545132,"score_gpt":0.213603867559465,"score_spread":0.1999814152340137,"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."}}