{"id":"W4298131893","doi":"","title":"Experimental comparison of Bayesian vehicle positioning methods based on multi-sensor data fusion","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Sensor fusion; Bayesian probability; Computer science; Fusion; Artificial intelligence; Computer vision","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":[0.002468645,0.0007736867,0.0007421153,0.001203118,0.0005485216,0.0009724378,0.001213483,0.001540604,0.003015392],"category_scores_gemma":[0.01033202,0.0004997248,0.0003601096,0.001172593,0.0005477893,0.001614918,0.00110874,0.0007163921,0.001026985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005306228,"about_ca_system_score_gemma":0.0007436508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003424612,"about_ca_topic_score_gemma":0.003129979,"domain_scores_codex":[0.9980748,0.0004630582,0.0001102941,0.0003155214,0.0008868872,0.0001495114],"domain_scores_gemma":[0.9939591,0.002459022,0.0004275023,0.0006848989,0.002327148,0.0001423776],"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":[0.007577095,0.0012403,0.01245726,0.001703257,0.000443702,0.0002207181,0.000755891,0.2086708,0.2742383,0.005961156,0.003780843,0.4829507],"study_design_scores_gemma":[0.000210766,0.002032212,0.02349191,0.0001008359,0.0001852475,0.0003270578,0.0002610667,0.8163458,0.1499706,0.002361941,0.004584639,0.000127936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4901997,0.001479046,0.4957947,0.0003880223,0.0005617084,0.0002523443,0.0007864615,0.001695841,0.008842091],"genre_scores_gemma":[0.9098828,0.000405594,0.08603795,0.00007219433,0.00003987383,0.0001209101,0.0007931805,0.0001688586,0.002478707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003424612,"threshold_uncertainty_score":0.01305562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05227378665101123,"score_gpt":0.3310364755847499,"score_spread":0.2787626889337387,"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."}}