{"id":"W2121601507","doi":"","title":"A fault tolerant state estimation framework with application to UGV navigation in complex terrain","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; AUG Signals (Canada)","funders":"","keywords":"Computer science; Sensor fusion; Kinematics; Fault detection and isolation; Terrain; Asynchronous communication; Fault tolerance; State (computer science); Artificial intelligence; Real-time computing; Algorithm; Actuator; Distributed computing","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":[0.0003200854,0.0001988253,0.0001528325,0.0004523708,0.0001202772,0.0002882403,0.0008070272,0.00009728524,0.0001963347],"category_scores_gemma":[0.00006690527,0.000174941,0.00003023005,0.0005072554,0.00003129671,0.001975485,0.0001534184,0.0002657683,0.0004805251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001254154,"about_ca_system_score_gemma":0.00005398274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002465576,"about_ca_topic_score_gemma":0.00003636712,"domain_scores_codex":[0.9981505,0.00005426479,0.0005809562,0.000308173,0.0006736097,0.0002324716],"domain_scores_gemma":[0.9986998,0.00005744763,0.0002905113,0.0003885361,0.0004497211,0.0001139347],"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":[0.000514371,0.0002203622,0.001344481,0.00002965043,0.00001807736,0.000007642403,0.02039971,0.05384443,0.0007466623,0.4576697,0.001045129,0.4641597],"study_design_scores_gemma":[0.0005646829,0.0002305666,0.02219803,0.0002875873,0.000001824314,0.00001605342,0.0002261805,0.9568967,0.0005617384,0.01420828,0.0045123,0.0002960693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05799925,0.000001035493,0.9301065,0.001139176,0.0002648246,0.0004592495,0.00003598693,0.0001935173,0.009800527],"genre_scores_gemma":[0.8833816,0.000006585413,0.1149985,0.0010924,0.00002408527,0.0001004778,0.0003555767,0.000006935044,0.00003381603],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9030523,"threshold_uncertainty_score":0.7133888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03237048624936621,"score_gpt":0.2817280471768246,"score_spread":0.2493575609274584,"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."}}