{"id":"W2148142904","doi":"10.1155/2013/382619","title":"A Novel Feature-Level Data Fusion Method for Indoor Autonomous Localization","year":2013,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National High-tech Research and Development Program; China Postdoctoral Science Foundation; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer vision; Position (finance); Monocular; Feature (linguistics); Computer science; Orientation (vector space); Sensor fusion; Enhanced Data Rates for GSM Evolution; Monocular vision; Fusion; Pattern recognition (psychology); Mathematics","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":[0.0003765697,0.0002366437,0.000301039,0.00017025,0.00003591144,0.00008959886,0.0003008709,0.0001828113,0.00003646046],"category_scores_gemma":[0.0002507187,0.0002259181,0.00004039144,0.000290134,0.0000111421,0.0002691658,0.00008666438,0.0001924484,0.00002620703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009916032,"about_ca_system_score_gemma":0.00001261935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000148921,"about_ca_topic_score_gemma":0.000006305573,"domain_scores_codex":[0.998731,0.000009806439,0.0004334439,0.0002865252,0.0001697703,0.0003694132],"domain_scores_gemma":[0.9991289,0.0002165728,0.00003471139,0.0004697947,0.00005672568,0.00009328582],"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":[8.757647e-7,0.00004837721,0.00001067337,0.0009688742,0.00001657618,4.181728e-7,0.0001343918,0.9807966,0.01041969,0.004055815,0.000701028,0.002846676],"study_design_scores_gemma":[0.0004610727,0.00001592762,0.00003635679,0.0002290203,0.00001482946,0.000008524836,0.00001448284,0.9945151,0.0008791641,0.002644464,0.0009075949,0.0002734627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002237406,0.00007414823,0.9979069,0.0001045413,0.0001631839,0.0009620753,0.00003472949,0.0003285304,0.0002021717],"genre_scores_gemma":[0.2442149,0.00001345523,0.7548527,0.00005015023,0.0001004746,0.0002677405,0.0002693616,0.0001365505,0.0000946653],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2439911,"threshold_uncertainty_score":0.9212672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04221864116947354,"score_gpt":0.256470281031031,"score_spread":0.2142516398615575,"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."}}