{"id":"W1979503369","doi":"10.1109/itsc.2007.4357638","title":"Improving Vehicle Positioning and Visual Feature Estimates through Mutual Constraint","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Position (finance); Global Positioning System; Constraint (computer-aided design); Computer science; Feature (linguistics); Artificial intelligence; Visualization; Computer vision; Bayesian probability; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002879212,0.001823141,0.00160652,0.001457824,0.0008133821,0.001516673,0.002758606,0.001746319,0.001919657],"category_scores_gemma":[0.01925563,0.001076299,0.001064378,0.00237761,0.00129202,0.004605487,0.004570346,0.00182201,0.000961358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030838,"about_ca_system_score_gemma":0.001707848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009666558,"about_ca_topic_score_gemma":0.007178306,"domain_scores_codex":[0.9967885,0.0009629143,0.0001594404,0.0006566605,0.001182344,0.0002501084],"domain_scores_gemma":[0.9942647,0.0029187,0.0008352186,0.0009555408,0.0009173917,0.0001085062],"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":[0.0002657007,0.00008912558,0.001693283,0.0001542542,0.0001450184,0.0001278127,0.0003168255,0.7404643,0.008735491,0.03468256,0.001827642,0.211498],"study_design_scores_gemma":[0.00002812585,0.00007229362,0.0007097571,0.00001806732,0.00003274332,0.00007525575,0.00003290597,0.977591,0.0040416,0.01561358,0.001744676,0.00003996479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007083972,0.0001033825,0.9910756,0.0001403018,0.0000169434,0.00001954311,0.00005456186,0.0002719529,0.001233647],"genre_scores_gemma":[0.5634223,0.000290306,0.432147,0.000190754,0.000103644,0.0001780055,0.0005155122,0.0001895747,0.002962967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009666558,"threshold_uncertainty_score":0.01922059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00919495397747208,"score_gpt":0.2609781876478673,"score_spread":0.2517832336703952,"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."}}