{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002019727,0.00006991062,0.0001040893,0.0001033956,0.00002779721,0.00002949876,0.00007552875,0.00005279326,0.000402465],"category_scores_gemma":[0.00000955891,0.00006208802,0.00001512306,0.0001236095,0.00004520508,0.0001284481,0.00004197419,0.00004447083,0.00001439426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009865235,"about_ca_system_score_gemma":0.00000210815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006895216,"about_ca_topic_score_gemma":0.000002357406,"domain_scores_codex":[0.9996419,0.000004725835,0.000133441,0.00006072705,0.00005207036,0.0001071353],"domain_scores_gemma":[0.9998015,0.00001661591,0.00001336031,0.0001150784,0.0000345553,0.0000188912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008839959,0.00006613966,0.1059226,0.0006683382,0.0001336179,0.000004913444,0.001165289,0.01250035,0.7948216,0.007413039,0.005768041,0.07152718],"study_design_scores_gemma":[0.0006790046,0.00009311478,0.1071969,0.00007411921,0.00003018051,0.00002774177,0.0007122791,0.3830468,0.5046088,0.002922902,0.0002002514,0.0004078701],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791507,0.0001732772,0.01783452,0.000032011,0.00006706725,0.00009429546,0.000001541894,0.0002523532,0.002394216],"genre_scores_gemma":[0.9892858,0.00001913321,0.01060969,0.00002229189,0.000008168214,0.000005779293,0.000001751269,0.00001091009,0.00003650926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3705464,"threshold_uncertainty_score":0.4406709,"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."}}