{"id":"W2621386686","doi":"10.3390/s17061272","title":"A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Calgary","keywords":"Inertial measurement unit; Particle filter; Extended Kalman filter; Computer science; Inertial navigation system; Real-time computing; Map matching; Kalman filter; Indoor positioning system; Global Positioning System; Computer vision; Artificial intelligence; Orientation (vector space); Accelerometer; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008722229,0.0001595069,0.0001671556,0.0001113197,0.0003575574,0.0001111572,0.0003930343,0.0001652963,0.00001029978],"category_scores_gemma":[0.0000644646,0.0001541876,0.00004836963,0.0001624581,0.00004985596,0.00007227543,0.00002002976,0.0001083337,0.0002037478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001184415,"about_ca_system_score_gemma":0.00004151301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007471747,"about_ca_topic_score_gemma":0.0001206185,"domain_scores_codex":[0.9992722,0.000009189736,0.0002171748,0.0001825533,0.00009688341,0.0002220409],"domain_scores_gemma":[0.9988336,0.00005534263,0.00007924972,0.0006906837,0.0002960367,0.0000450733],"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":[0.0001328928,0.0001675788,0.004780257,0.008442095,0.0004141402,0.00001422433,0.001204689,0.848695,0.007910212,0.06338701,0.01280316,0.05204879],"study_design_scores_gemma":[0.000913701,0.00002186904,0.0005072968,0.000145398,0.00004552558,0.000003054785,0.001368386,0.8405268,0.07719393,0.0002130406,0.078673,0.0003880181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1163359,0.0002550643,0.8644481,0.00189651,0.001239838,0.003047248,0.0003962068,0.006774008,0.005607126],"genre_scores_gemma":[0.9964198,0.000003339119,0.002668135,0.00006811169,0.000079648,0.0004311673,0.0001068449,0.00004539416,0.0001774899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8800839,"threshold_uncertainty_score":0.6287586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283713126778806,"score_gpt":0.2310715795134802,"score_spread":0.2182344482456922,"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."}}