{"id":"W2094957730","doi":"10.1109/plans.2014.6851481","title":"Autonomous WLAN heading and position for smartphones","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"Heading (navigation); GNSS applications; Computer science; RSS; Fingerprint (computing); Real-time computing; Wi-Fi; Process (computing); Compass; Global Positioning System; Hybrid positioning system; Wireless; Position (finance); Satellite system; Positioning system; Fingerprint recognition; Wireless network; Artificial intelligence; Telecommunications; Node (physics); Engineering; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001970456,0.0004625507,0.0003496425,0.0006709705,0.0002305381,0.0004650805,0.0004070142,0.0003171406,0.002029875],"category_scores_gemma":[0.0009996621,0.00018627,0.0001947756,0.0003833316,0.0001196506,0.0004506042,0.0005737739,0.0002620624,0.001694202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002093798,"about_ca_system_score_gemma":0.0002204112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002341545,"about_ca_topic_score_gemma":0.002656257,"domain_scores_codex":[0.999787,0.00003498568,0.00001205113,0.0000461493,0.00009169696,0.00002812991],"domain_scores_gemma":[0.9996787,0.00004048576,0.00005228346,0.00006697905,0.0001348086,0.00002666426],"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":[0.0004937622,0.00007173503,0.01954518,0.0003009988,0.00003842869,0.0004114726,0.0003583743,0.01973378,0.0818451,0.003304474,0.01270819,0.8611885],"study_design_scores_gemma":[0.0002011026,0.001247925,0.0741265,0.0001849731,0.0002075037,0.002557033,0.0006520095,0.6887872,0.1338384,0.004427842,0.09357818,0.0001913711],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1456247,0.001972409,0.8074432,0.0003764753,0.0005516509,0.0001572027,0.0009565967,0.02678175,0.01613604],"genre_scores_gemma":[0.8554163,0.0006248628,0.1353315,0.0001028808,0.000155059,0.00008156337,0.0008007406,0.0002056139,0.007281553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002341545,"threshold_uncertainty_score":0.006790578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004785414426405841,"score_gpt":0.1880028259211853,"score_spread":0.1832174114947794,"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."}}