{"id":"W111456786","doi":"","title":"Assisting personal positioning in indoor environments using map matching","year":2011,"lang":"en","type":"article","venue":"Archives of Photogrammetry Cartography and Remote Sensing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Map matching; Computer science; Global Positioning System; Geospatial analysis; GNSS applications; Inertial navigation system; Real-time computing; Mobile mapping; USable; Computer vision; Matching (statistics); Position (finance); Artificial intelligence; Orientation (vector space); Remote sensing; Geography; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001116079,0.0002001742,0.0002520088,0.0007031877,0.0001283378,0.00002128825,0.00007983802,0.0001000636,0.000004108584],"category_scores_gemma":[0.00001116839,0.0002119136,0.0001208633,0.0003645975,0.0002461965,0.00009518259,0.00005808756,0.0002635889,4.044825e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001413025,"about_ca_system_score_gemma":0.000006084042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065905,"about_ca_topic_score_gemma":0.00007005226,"domain_scores_codex":[0.9989401,0.00004527464,0.0003257848,0.0002209637,0.000128126,0.000339775],"domain_scores_gemma":[0.9996526,0.00007591932,0.00007330724,0.0001325592,0.00000679029,0.00005886777],"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":[0.00008147768,0.00004443784,0.01901889,0.0003987716,0.000196847,0.0000803836,0.0161466,0.001093052,0.3248249,0.0001101709,0.000001678007,0.6380028],"study_design_scores_gemma":[0.001027985,0.0001081625,0.01949042,0.0009806657,0.0000868463,0.0001168865,0.006720751,0.7049193,0.2575652,0.008222098,0.00005745764,0.0007042706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6525474,0.0004568244,0.3446097,0.000002712231,0.0001160211,0.0001092361,0.000004713102,0.00009865411,0.002054777],"genre_scores_gemma":[0.9114063,0.0001009971,0.08842146,0.00001473789,0.00001924425,1.175181e-7,0.000006252609,0.00002900162,0.000001905082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7038262,"threshold_uncertainty_score":0.8641587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01333861016533478,"score_gpt":0.2001454026104215,"score_spread":0.1868067924450868,"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."}}