{"id":"W2016863885","doi":"10.1109/mownet.2013.6613810","title":"Indoor positioning using magnetic compass and accelerometer of smartphones","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"University of Windsor","keywords":"Compass; Global Positioning System; Accelerometer; Computer science; Tracking (education); Real-time computing; Indoor positioning system; Non-line-of-sight propagation; Positioning system; Software; Hybrid positioning system; Assisted GPS; Wireless; Acoustics; Telecommunications; Geography","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":[0.0001252914,0.0007279481,0.0005086377,0.0008000538,0.0002249749,0.0004289816,0.0005943553,0.0004581638,0.001713178],"category_scores_gemma":[0.0005366195,0.0002481627,0.0002903222,0.0008382846,0.000132447,0.0005188034,0.0005492985,0.0002568734,0.001588538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001371125,"about_ca_system_score_gemma":0.000174697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001692904,"about_ca_topic_score_gemma":0.002259051,"domain_scores_codex":[0.9995179,0.00007203707,0.00003267547,0.0001265815,0.0002065819,0.00004416567],"domain_scores_gemma":[0.9996982,0.00002883741,0.00006461211,0.00005178556,0.0001386343,0.00001803854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004737444,0.00007999112,0.01813943,0.001173833,0.0001378307,0.0009794144,0.000345515,0.005214843,0.299399,0.002735329,0.006995162,0.664326],"study_design_scores_gemma":[0.0001914787,0.003851248,0.1456239,0.0007480777,0.0007803483,0.01740008,0.0009925492,0.2097306,0.4186779,0.003639198,0.1978378,0.0005268765],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1425738,0.005991937,0.8202045,0.0003242585,0.0006589,0.0001828988,0.0009199068,0.00679869,0.02234514],"genre_scores_gemma":[0.8391176,0.002835079,0.14569,0.0001606668,0.0002568824,0.0001223983,0.0007654688,0.00005536879,0.01099649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001713178,"threshold_uncertainty_score":0.005731165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009373740846721657,"score_gpt":0.1872917716610971,"score_spread":0.1779180308143755,"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."}}