{"id":"W2326421185","doi":"10.17265/2328-2142/2016.01.004","title":"Low-Cost GPS/INS Integrated Land-Vehicular Navigation System for Harsh Environments Using Hybrid Mamdani AFIS/KF Model","year":2016,"lang":"en","type":"article","venue":"Journal of Traffic and Transportation Engineering","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; Computer science; Transport engineering; Real-time computing; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002254866,0.0005106275,0.0004918876,0.000187061,0.0004458797,0.0004235996,0.0006491074,0.0004914859,0.0009459286],"category_scores_gemma":[0.0002718603,0.0002359729,0.0004438571,0.0002169822,0.0001700982,0.0005912315,0.0003536688,0.0005281777,0.0003805803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003544421,"about_ca_system_score_gemma":0.0005900636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01074585,"about_ca_topic_score_gemma":0.0115184,"domain_scores_codex":[0.9998446,0.00002140954,0.00001125036,0.00003908589,0.00006535619,0.00001836481],"domain_scores_gemma":[0.9999087,0.00001614924,0.00001239514,0.000008396638,0.00004903265,0.000005344685],"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":[0.0003272513,0.0001067813,0.004822789,0.0002977273,0.0001477089,0.0002370677,0.0002679724,0.6894792,0.06028863,0.004482136,0.002124139,0.2374187],"study_design_scores_gemma":[0.00001008804,0.00006312622,0.0006154851,0.000006996986,0.00002460498,0.00003372795,0.00001675579,0.993726,0.004226717,0.0003705777,0.0008957471,0.00001023427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04486582,0.0003463906,0.950084,0.000120536,0.00009599492,0.00003798192,0.00006345954,0.0006804668,0.003705345],"genre_scores_gemma":[0.9096523,0.0002474042,0.08540194,0.00004513681,0.00003041092,0.00008762016,0.000134946,0.00002296336,0.004377201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01074585,"threshold_uncertainty_score":0.0213666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007577545586205527,"score_gpt":0.1884114908222073,"score_spread":0.1808339452360017,"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."}}