{"id":"W3108914020","doi":"10.1109/access.2020.3039271","title":"A Survey of Machine Learning for Indoor Positioning","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":258,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Scalability; Adaptability; Non-line-of-sight propagation; Software deployment; Wireless; Machine learning; Artificial intelligence; Telecommunications","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":[0.00008403207,0.00007424979,0.0001353878,0.00005015285,0.00004304494,0.0000330195,0.0002109714,0.00005935385,0.00002058022],"category_scores_gemma":[0.0001944196,0.00007521809,0.00002965239,0.0002780849,0.000017805,0.0001373663,0.00002426419,0.0001002941,0.000004105001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001048685,"about_ca_system_score_gemma":0.000007264402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007617223,"about_ca_topic_score_gemma":0.00003095227,"domain_scores_codex":[0.9995586,0.0000145287,0.0001589612,0.00008715474,0.0000650777,0.0001157094],"domain_scores_gemma":[0.999718,0.00008413199,0.00003402486,0.00006455631,0.00007401525,0.00002530191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001198453,0.00002994299,0.2544267,0.0008671574,0.0001668581,0.000005213538,0.0009802295,0.6870738,0.02349087,0.0005748157,0.004392203,0.02787229],"study_design_scores_gemma":[0.0005564485,0.00007600054,0.01769541,0.00002568978,0.00001369936,7.835166e-7,0.00002900294,0.6563599,0.3241271,0.0001032829,0.0008205976,0.00019213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3610759,0.0003600254,0.6365055,0.0001171308,0.0002364571,0.0002311072,0.00006348274,0.0008159964,0.0005944215],"genre_scores_gemma":[0.9994905,0.00002294033,0.0002922524,0.00007234643,0.00003078268,0.00001533483,0.00004578034,0.00002171343,0.000008352146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6384146,"threshold_uncertainty_score":0.3067305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04411765217212168,"score_gpt":0.2787286757262707,"score_spread":0.234611023554149,"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."}}