{"id":"W2169993879","doi":"10.1109/itsc.2006.1707353","title":"Co-operative lane-level positioning using Markov localization","year":2006,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Global Positioning System; Position (finance); Computer science; Markov chain; Intelligent transportation system; Path (computing); Real-time computing; Hidden Markov model; Markov process; Bounded function; Artificial intelligence; Computer vision; Engineering; Mathematics; Machine learning; Transport engineering; Computer network; 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":[],"consensus_categories":[],"category_scores_codex":[0.00007619144,0.0001274162,0.0001105082,0.00005519978,0.0001014759,0.00006753155,0.00007777511,0.00007517447,0.000278582],"category_scores_gemma":[0.000004690356,0.0001303068,0.00003030548,0.0001435905,0.00001740122,0.0001632165,0.00001197507,0.00009465856,0.00006411388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205221,"about_ca_system_score_gemma":0.00001091269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001518946,"about_ca_topic_score_gemma":0.0001212653,"domain_scores_codex":[0.9993275,0.00002483662,0.0001659828,0.0001244812,0.0001263377,0.0002308533],"domain_scores_gemma":[0.9997342,0.00001928354,0.00001536436,0.0001569773,0.00003467751,0.00003947514],"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":[0.000001558945,0.000006740808,0.0006804533,0.0000108583,0.0000142623,0.00001019229,0.00002793171,0.9869241,0.004732209,0.0009159409,0.006416452,0.000259299],"study_design_scores_gemma":[0.0001770423,0.000006685003,0.0009540789,0.00002744439,0.00001140206,0.00002795963,0.00002108654,0.9903683,0.006580906,0.0001677805,0.001477875,0.0001794389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07988103,0.0001820243,0.8746625,0.00002920303,0.0001371665,0.0001492159,0.00001189532,0.0004264482,0.04452047],"genre_scores_gemma":[0.978776,0.00000628658,0.02024559,0.00008940749,0.0002253553,0.000004494297,0.0001721654,0.00004081898,0.0004398657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.898895,"threshold_uncertainty_score":0.5313756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224199049292761,"score_gpt":0.2365842088637901,"score_spread":0.2243422183708625,"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."}}