{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004801379,0.000409235,0.0005768377,0.0002601843,0.0004652033,0.0006312474,0.004723411,0.0003579284,0.0001621865],"category_scores_gemma":[0.0008094754,0.0004236608,0.0001739154,0.0004128852,0.0002017808,0.0003358313,0.004997937,0.0008678227,0.00003579677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009023125,"about_ca_system_score_gemma":0.0001675372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008910534,"about_ca_topic_score_gemma":0.0000638706,"domain_scores_codex":[0.9893013,0.007388344,0.0008625769,0.001397547,0.0006465201,0.0004037138],"domain_scores_gemma":[0.9887506,0.002465373,0.0008576866,0.006645338,0.001054416,0.0002266058],"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":[0.0001285158,0.0147997,0.009095031,0.000752031,0.0003193675,0.00002888158,0.03299383,0.03625248,0.1854607,0.1020091,0.02269949,0.5954609],"study_design_scores_gemma":[0.0004543458,0.000001408652,0.000848937,0.001174569,0.00001613699,0.000002008039,0.00008283676,0.8399081,0.1553759,0.0001928858,0.001606554,0.0003363391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01381916,0.0007727171,0.9780821,0.002165113,0.0005724264,0.0004517217,0.0001422244,0.0003451866,0.00364933],"genre_scores_gemma":[0.4397339,0.00002447994,0.5588924,0.00008586761,0.00001853156,0.00002275313,0.0009559403,0.00002650383,0.0002395917],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8036556,"threshold_uncertainty_score":0.9998215,"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."}}