{"id":"W2182261117","doi":"10.1109/ipin.2015.7346761","title":"GIPSy: Geomagnetic indoor positioning system for smartphones","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Viterbi algorithm; Computer science; Orientation (vector space); Real-time computing; Tracking (education); Set (abstract data type); Gesture; State (computer science); Computer vision; Algorithm; Artificial intelligence; Hidden Markov model","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008541068,0.00009637627,0.0001128995,0.00008730804,0.00004960674,0.00004065253,0.0001006578,0.00008797504,0.00001452543],"category_scores_gemma":[0.00003263083,0.00008763248,0.00003388949,0.0001346974,0.00001919128,0.00008163082,0.00001644148,0.00004477092,0.00008047229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007521895,"about_ca_system_score_gemma":0.00001197936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001170035,"about_ca_topic_score_gemma":0.000006814737,"domain_scores_codex":[0.9994831,0.000005208989,0.0001510262,0.00009329808,0.0000834103,0.0001839291],"domain_scores_gemma":[0.9996993,0.00002395461,0.00001331306,0.0001356189,0.00007870481,0.0000491407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001839979,0.0001288919,0.009680362,0.003225688,0.0003995631,0.00005666874,0.003622528,0.2059759,0.0202152,0.4023685,0.2730468,0.08109583],"study_design_scores_gemma":[0.004049132,0.0004339115,0.0009479446,0.0001920487,0.00009247886,0.0001172552,0.009859663,0.5548815,0.3792552,0.003756619,0.04522255,0.001191759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1319482,0.0008667339,0.8003381,0.0002001212,0.001841857,0.0006324816,0.00002225854,0.007631711,0.05651854],"genre_scores_gemma":[0.9933789,0.00000351837,0.00600796,0.00002868908,0.00007104476,0.0000752836,0.00001623449,0.00002492127,0.0003934168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8614308,"threshold_uncertainty_score":0.3573549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646379425418495,"score_gpt":0.2024538028524162,"score_spread":0.1859900085982313,"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."}}